Researcher basic information

• 博士（情報科学）, 北海道大学

• https://researchmap.jp/k_maeda_k?lang=en

• https://jglobal.jst.go.jp/detail?JGLOBAL_ID=201901006500685453
• https://www-lmd.ist.hokudai.ac.jp/member/keisuke-maeda/

• 201901006500685453

• Artificial Intelligence
• マルチモーダル
• 視線
• 可視化
• fNIRS
• 衛星画像
• 社会基盤
• スポーツ映像
• SNS
• マルチスペクトル解析
• 深層学習
• 行動解析
• Image processing
• Signal processing
• Machine learning

• Informatics, Perceptual information processing
• Informatics, Intelligent informatics
• Informatics, Theory of informatics

• Bachelor's degree program, School of Engineering
• Master's degree program, Graduate School of Information Science and Technology
• Doctoral (PhD) degree program, Graduate School of Information Science and Technology

Research activity information

• Jun. 2024, デジタルツイン・DX奨励賞 (五箇ほか, AI・データサイエンス論文集, 2024)
• Feb. 2024, The 2023 IEEE Sapporo Section Encouragement Award (Y. Moroto et al., IEEE ICASSP, 2022)
• Feb. 2024, The 2023 IEEE Sapporo Section Student Paper Contest Encouraging Prize (M. Kashiwagi et al.電気・情報関係学会北海道支部連合大会, 2023)
• Jan. 2024, Best Paper Award (R. Goka et al., IWAIT, 2024)
• Dec. 2023, 2023年 AI・データサイエンス賞【AI・データサイエンス論文賞】(櫻井ほか, AI・データサイエンス論文集, 2023)
• Dec. 2023, 2023年 AI・データサイエンス賞【AI・データサイエンス奨励賞】(諸戸ほか, AI・データサイエンス論文集, 2023)
• Dec. 2023, 電気・情報関係学会北海道支部連合大会, 2023, 令和5年度電気・情報関係学会北海道支部連合大会若手優秀論文発表賞 (T. Togo et al.)
• Dec. 2023, 電気・情報関係学会北海道支部連合大会, 2023, 令和5年度電気・情報関係学会北海道支部連合大会若手優秀論文発表賞 (M. Sato et al.)
• Dec. 2023, 電気・情報関係学会北海道支部連合大会, 令和5年度電気・情報関係学会北海道支部連合大会若手優秀論文発表賞 (H. Matsuda et al.)
• Oct. 2023, Silver Prize GCCE2023 Excellent Paper Award (H. Matsuda et al., IEEE GCCE 2023)
• Jul. 2023, Best Paper Award (Honorable Mention) (R. Goka et al., ICCE-TW,2023)
• Jan. 2023, The 2022 IEEE Sapporo Section Encouragement Award (K. Kamikawa et al., IEEE Access, 2021)
• Jan. 2023, The 2022 IEEE Sapporo Section Encouragement Award (N. Ogawa et al., IEEE Access, 2021)
• Dec. 2022, 電気・情報関係学会北海道支部連合大会, 2022, 令和4年度電気・情報関係学会北海道支部連合大会若手優秀論文発表賞 (R. Goka et al.)
• Dec. 2022, 電気・情報関係学会北海道支部連合大会, 2022, 令和4年度電気・情報関係学会北海道支部連合大会若手優秀論文発表賞 (R. Shichida et al.)
• Dec. 2022, 電気・情報関係学会北海道支部連合大会, 2022, 令和4年度電気・情報関係学会北海道支部連合大会若手優秀論文発表賞(K. Yamamoto et al.)
• Nov. 2022, 2022 IEEE Sapporo Young Professionals Best Researcher Award (K. Maeda)
• Nov. 2022, IEEE GCCE2022, Excellent Student Paper Awards, Bronze Prize (K. Yamamoto et al.)
• Oct. 2022, IEEE GCCE2022, Excellent Student Poster Award, Silver Prize (Y. Era et al.)
• Feb. 2022, IEEE Sapporo section, The 2021 IEEE Sapporo Section Encouragement Award
  Human-centric emotion estimation based on correlation maximization considering changes with time in visual attention and brain activity
  Yuya Moroto;Keisuke Maeda;Takahiro Ogawa;Miki Haseyama
• Feb. 2022, IEEE Sapporo section, 2021 IEEE Sapporo Section Student Paper Contest
  Few-shot Learningを用いた感情ラベル推定における複数のデータセット利用に関する初期検討
  叶 穎睿;諸戸 祐哉;前田 圭介;小川 貴弘;長谷山 美紀
• Dec. 2021, 映像情報メディア学会, 映像情報メディア学会 優秀研究発表賞
  Attention Map を用いた道路構造物の変状画像分類におけるテキストデータの導入に基づく高精度化に関する検討
  小川 直輝;前田 圭介;小川 貴弘;長谷山 美紀
• Nov. 2021, 電気・情報関係学会北海道支部連合大会, 令和3年度 電気・情報関係学会北海道支部連合大会 若手優秀論文発表賞
  Shilling attackの状況下におけるグラフ解析に基づく推薦システムの脆弱性の検証
  小野寺 望;前田 圭介;小川 貴弘;長谷山 美紀
• Oct. 2021, IEEE GCCE, IEEE GCCE2021 Excellent Student Poster Award Gold Prize
  Analysis of social trends related to COVID-19 pandemic utilizing social media data
  Taisei Hirakawa;Keisuke Maeda;Takahiro Ogawa;Satoshi Asamizu;Miki Haseyama
• Oct. 2021, IEEE GCCE, IEEE GCCE2021 Outstanding Paper Award
  Multi-label image recognition based on multi-modal graph convolutional networks using captioning features
  Ziwen Lan;Keisuke Maeda;Takahiro Ogawa;Miki Haseyama
• Aug. 2021, IEEE Sapporo section, 2021 IEEE Sapporo Young Professionals Best Paper Award
  Estimation of deterioration levels of transmission towers via deep learning maximizing canonical correlation between heterogeneous features
  Keisuke Maeda
• Mar. 2021, IEEE LifeTech, 3rd Prize IEEE LifeTech 2021 Excellent Paper Award for On-site Poster Presentation
  Human emotion estimation using multi-modal variational autoencoder with time changes
  Yuya Moroto;Keisuke Maeda;Takahiro Ogawa;Miki Haseyama
• Nov. 2020, 電気・情報関係学会北海道支部連合大会, 令和2年度電気・情報関係学会北海道支部連合大会若手優秀論文発表賞
  路面画像を用いた異常検知に基づく路面状態の識別に関する検討
  諸戸 祐哉;前田 圭介;小川 貴弘;長谷山 美紀
• Nov. 2020, 電気・情報関係学会北海道支部連合大会, 令和2年度電気・情報関係学会北海道支部連合大会若手優秀論文発表賞
  多層グラフ解析に基づくクロスドメイン推薦に関する検討 – 埋め込み特徴量の次元数の変化による精度検証 -
  平川 泰成;前田 圭介;小川 貴弘;浅水 仁;長谷山 美紀
• Nov. 2020, 電気・情報関係学会北海道支部連合大会, 令和2年度電気・情報関係学会北海道支部連合大会若手優秀論文発表賞
  画像注視時のfMRIデータを用いた注視画像の推定に関する検討ー確率的生成モデルに基づく複数被験者の共通応答の導入ー
  東 孝明;前田 圭介;小川 貴弘;長谷山 美紀
• Oct. 2020, IEEE GCCE, The 2020 IEEE GCCE Silver Prize GCCE2020 Excellent Paper Award
  Important scene prediction of baseball videos using twitter and video analysis based on LSTM
  Kaito Hirasawa;Keisuke Maeda;Takahiro Ogawa;Miki Haseyama
• Oct. 2020, IEEE GCCE, The 2020 IEEE GCCE Bronzer Prize GCCE2020 Excellent Paper Award
  Interest level estimation based on feature integration considering distribution of partially paired user’s behavior, videos and posters
  Kyohei Kamikawa;Keisuke Maeda;Takahiro Ogawa;Miki Haseyama
• Sep. 2020, IEEE ICCE-TW, The 2020 IEEE ICCE-TW Honorable Mention for Best Paper Award
  Important scene detection based on anomaly detection using long short-term memory for baseball highlight generation
  Kaito Hirasawa;Keisuke Maeda;Takahiro Ogawa;Miki Haseyama
• Feb. 2020, IEEE Sapporo section, The 2019 IEEE Sapporo Section Encouragement Award
  Estimation of deterioration levels of transmission towers via deep learning maximizing canonical correlation between heterogeneous features
  Keisuke Maeda;Sho Takahashi;Takahiro Ogawa;Miki Haseyama
• Feb. 2020, IEEE Sapporo section, The 2019 IEEE Sapporo Section Student Paper Contest Encouraging Prize
  Sparse Bayesian Learning に基づく注視領域の時間変化を考慮したヒトの感情推定に関する検討
  諸戸 祐哉;前田 圭介;小川 貴弘;長谷山 美紀
• Feb. 2020, IEEE Sapporo section, The 2019 IEEE Sapporo Section Student Paper Contest Encouraging Prize
  変状分類における Grad-CAM++ に基づいた CNN の注目領域の可視化に関する検討
  小川 直輝;前田 圭介;小川 貴弘;長谷山 美紀
• Dec. 2019, 映像情報メディア学会, 映像情報メディア学会 優秀研究発表賞
  畳み込みニューラルネットワークにおける解釈性向上のための画像の属性分類に関する一検討
  堀井 風葉;前田 圭介;小川 貴弘;長谷山 美紀
• Mar. 2019, 2nd Prize IEEE LifeTech 2019 Excellent Paper Award
  Estimation of Visual Attention via Canonical Correlation between Visual and Gaze-based Features
  Keisuke MAEDA, 共著者として受賞
• Feb. 2019, The 2018 IEEE Sapporo Section Encouragement Award
  Automatic Estimation of Deterioration Level on Transmission Towers via Deep Extreme Learning Machine Based on Local Receptive Field
  Keisuke MAEDA
• Dec. 2018, 映像情報メディア学会 優秀研究発表賞
  道路構造物に発生する変状の自動分類の高精度化に向けたConvolutional Sparse Coding の導入に関する検討
  前田 圭介
• Oct. 2018, IEEE GCCE 2018 Outstanding Paper Award
  User-centric Visual Attention Estimation Based on Relationship Between Image and Eye Gaze Data
  Keisuke MAEDA, 共著者として受賞
• Apr. 2018, 2017 IBM Ph. D. Fellowship award
  Keisuke MAEDA
• Feb. 2018, The 2017 IEEE Sapporo Section Encouragement Award
  Distress Classification of Road Structures via Decision Level Fusion
  Keisuke MAEDA
• Feb. 2018, 平成29年度電気・情報関係学会北海道支部連合大会優秀論文発表賞
  画像特徴量と fNIRS 特徴量の関連性に注目した画像分類の高精度化に関する検討
  前田 圭介, 共著者として受賞
• Sep. 2017, 精密工学会画像応用技術専門委員会・映像情報メディア学会メディア工学研究委員会合同サマーセミナー 優秀発表賞
  正準相関最大化を導入した深層学習に基づく送電鉄塔の劣化レベル分類に関する検討
  前田 圭介
• Feb. 2017, 平成27年度電気・情報関係学会北海道支部連合大会優秀論文発表賞
  個々の道路構造物に関する点検項目の導入による 道路構造物の変状推定の高精度化に関する検討
  前田 圭介
• Feb. 2016, The 2015 IEEE Sapporo Section Encouragement Award
  Bayesian Network-based Distress Estimation Using Image Features in Road Structure Assessment
  Keisuke MAEDA

• Automated Dataset Construction for Composed Video Retrieval in Soccer
  Riku Yoshida; Ryota Goka; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  Applied Sciences, 27 May 2026
  Scientific journal
• Leveraging Attack Non-Transferability to Boost Adversarial Robustness for Foundation Models
  Koshiro Toishi; Keisuke Maeda; Ren Togo; Takahiro Ogawa; Miki Haseyama
  Applied Sciences, 17 Apr. 2026
  Scientific journal
• Discrete Prompt Tuning via Recursive Utilization of Black-Box Multimodal Large Language Model for Personalized Visual Emotion Recognition
  Ryo Takahashi; Naoki Saito; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  IEEE Access, 2026
  Scientific journal
• Deep Generative Replay-Based Personalization With Conditional Latent Attention for Diffusion Models
  Haruka Matsuda; Ren Togo; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  IEEE Access, 2026
  Scientific journal
• Generalizing Stylized Motion Generation Method by Introducing Metadata-Independent Learning and Unified Multiple Motion Dataset
  Yuki Era; Ren Togo; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  IEEE Transactions on Multimedia, 2026
  Scientific journal
• Objectness Similarity: Capturing Object-Level Fidelity in 3D Scene Evaluation.
  Yuiko Uchida; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  CoRR, abs/2509.09143, Sep. 2025
  Scientific journal
• GeoJapan Fusion Framework: A Large Multimodal Model for Regional Remote Sensing Recognition
  Yaozong Gan; Guang Li; Ren Togo; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  Remote Sensing, 17, 17, 3044, 3044, MDPI AG, 01 Sep. 2025, [Peer-reviewed], [International Magazine]
  Scientific journal, Recent advances in large multimodal models (LMMs) have opened new opportunities for multitask recognition from remote sensing images. However, existing approaches still face challenges in effectively recognizing the complex geospatial characteristics of regions such as Japan, where its location along the seismic belt leads to highly diverse urban environments and cityscapes that differ from those in other regions. To overcome these challenges, we propose the GeoJapan Fusion Framework (GFF), a multimodal architecture that integrates a large language model (LLM) and a vision–language model (VLM) and strengthens multimodal alignment ability through an in-context learning mechanism to support multitask recognition for Japanese remote sensing images. The GFF also incorporates a cross-modal feature fusion mechanism with low-rank adaptation (LoRA) to enhance representation alignment and enable efficient model adaptation. To facilitate the construction of the GFF, we construct the GeoJapan dataset, which comprises a substantial collection of high-quality Japanese remote sensing images, designed to facilitate multitask recognition using LMMs. We conducted extensive experiments and compared our method with state-of-the-art LMMs. The experimental results demonstrate that GFF outperforms previous approaches across multiple tasks, demonstrating its promising ability for multimodal multitask remote sensing recognition.
• Analysis of Model Merging Methods for Continual Updating of Foundation Models in Distributed Data Settings
  Kenta Kubota; Ren Togo; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  Applied Sciences, 15, 9, 5196, 5196, MDPI AG, 07 May 2025, [Peer-reviewed], [International Magazine]
  Scientific journal, Foundation models have achieved remarkable success across various domains, but still face critical challenges such as limited data availability, high computational requirements, and rapid knowledge obsolescence. To address these issues, we propose a novel framework that integrates model merging with federated learning to enable continual foundation model updates without centralizing sensitive data. In this framework, each client fine-tunes a local model, and the server merges these models using multiple merging strategies. We experimentally evaluate the effectiveness of these methods using the CLIP model for image classification tasks across diverse datasets. The results demonstrate that advanced merging methods can surpass simple averaging in terms of accuracy, although they introduce challenges such as catastrophic forgetting and sensitivity to hyperparameters. This study defines a realistic and practical problem setting for decentralized foundation model updates, and provides a comparative analysis of merging techniques, offering valuable insights for scalable and privacy-preserving model evolution in dynamic environments.
• Hyperbolic Dataset Distillation.
  Wenyuan Li; Guang Li 0008; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  CoRR, abs/2505.24623, May 2025, [Peer-reviewed], [International Magazine]
  Scientific journal
• Enhancing Adversarial Defense via Brain Activity Integration Without Adversarial Examples
  Tasuku Nakajima; Keisuke Maeda; Ren Togo; Takahiro Ogawa; Miki Haseyama
  Sensors, 25, 9, 2736, 2736, MDPI AG, 25 Apr. 2025, [Peer-reviewed], [International Magazine]
  Scientific journal, Adversarial attacks on large-scale vision–language foundation models, such as the contrastive language–image pretraining (CLIP) model, can significantly degrade performance across various tasks by generating adversarial examples that are indistinguishable from the original images to human perception. Although adversarial training methods, which train models with adversarial examples, have been proposed to defend against such attacks, they typically require prior knowledge of the attack. These methods also lead to a trade-off between robustness to adversarial examples and accuracy for clean images. To address these challenges, we propose an adversarial defense method based on human brain activity data by hypothesizing that such adversarial examples are not misrecognized by humans. The proposed method employs an encoder that integrates the features of brain activity and augmented images from the original images. Then, by maximizing the similarity between features predicted by the encoder and the original visual features, we obtain features with the visual invariance of the human brain and the diversity of data augmentation. Consequently, we construct a model that is robust against adversarial attacks and maintains accuracy for clean images. Unlike existing methods, the proposed method is not trained on any specific adversarial attack information; thus, it is robust against unknown attacks. Extensive experiments demonstrate that the proposed method significantly enhances robustness to adversarial attacks on the CLIP model without degrading accuracy for clean images. The primary contribution of this study is that the performance trade-off can be overcome using brain activity data.
• Generative Dataset Distillation Based on Self-knowledge Distillation
  Longzhen Li; Guang Li; Ren Togo; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), abs/2501.04202, 1, 5, IEEE, 06 Apr. 2025, [Peer-reviewed], [International Magazine]
  International conference proceedings
• Personalized Federated Learning for Egocentric Video Gaze Estimation with Comprehensive Parameter Frezzing.
  Yuhu Feng; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  CoRR, abs/2502.18123, Feb. 2025, [International Magazine]
  Scientific journal
• StarMAP: Global Neighbor Embedding for Faithful Data Visualization.
  Koshi Watanabe; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  CoRR, abs/2502.03776, Feb. 2025
  Scientific journal
• Damage-level classification considering both correlation between image and text data and confidence of attention map.
  Keisuke Maeda; Naoki Ogawa; Takahiro Ogawa 0001; Miki Haseyama
  Comput. Aided Civ. Infrastructure Eng., 40, 6, 764, 781, Feb. 2025, [Peer-reviewed], [Lead author], [International Magazine]
  Scientific journal
• Improving Robustness of CLIP by Adversarial Training Enhanced by Brain Activity
  Tasuku Nakajima; Keisuke Maeda; Ren Togo; Takahiro Ogawa; Miki Haseyama
  INTERNATIONAL WORKSHOP ON ADVANCED IMAGING TECHNOLOGY, IWAIT 2025, 13510, 2025
  English, International conference proceedings
• Balancing Generalization and Personalization by Sharing Layers in Clustered Federated Learning
  Kenta Kubota; Ren Togo; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  INTERNATIONAL WORKSHOP ON ADVANCED IMAGING TECHNOLOGY, IWAIT 2025, 13510, 2025
  English, International conference proceedings
• Enhanced Framework for Generating Counterfactual Images with Sophisticated Caption and Inversion-free Image Editing
  Xiang Li; Ren Togo; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  INTERNATIONAL WORKSHOP ON ADVANCED IMAGING TECHNOLOGY, IWAIT 2025, 13510, 2025
  English, International conference proceedings
• Enhancing Classification Models With Sophisticated Counterfactual Images
  Xiang Li; Ren Togo; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  IEEE Open Journal of Signal Processing, 6, 89, 98, Institute of Electrical and Electronics Engineers (IEEE), 2025, [Peer-reviewed], [International Magazine]
  Scientific journal
• Automatic generation of findings using generative AI to support for inspection report creation -Introduction of in-context learning based on similar image retrieval through cluster analysis-
  佐藤雅也; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀
  AI・データサイエンス論文集(Web), 6, 1, 2025, [Peer-reviewed], [Domestic magazines]
• Damage level estimation of inspection images in road infrastructures using in-context learning with data augmentation
  中島佑; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀
  AI・データサイエンス論文集(Web), 6, 1, 2025, [Peer-reviewed], [Domestic magazines]
• Triplet Synthesis For Enhancing Composed Image Retrieval via Counterfactual Image Generation.
  Kenta Uesugi; Naoki Saito 0006; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  CoRR, abs/2501.13968, Jan. 2025
  Scientific journal
• Triplet Synthesis for Enhancing Composed Image Retrieval via Counterfactual Image Generation.
  Kenta Uesugi; Naoki Saito 0006; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  ICASSP, 1, 5, 2025, [Peer-reviewed], [International Magazine]
  International conference proceedings
• Gradient-Oriented Clustered Federated Learning With Efficient Knowledge Sharing in Non-IID Settings.
  Kenta Kubota; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  ICASSP, 1, 5, 2025, [Peer-reviewed], [International Magazine]
  International conference proceedings
• Hyperboloid GPLVM for Discovering Continuous Hierarchies via Nonparametric Estimation.
  Koshi Watanabe; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  AISTATS, 1783, 1791, 2025, [Peer-reviewed], [International Magazine]
  International conference proceedings
• Linear Structure Analysis of Embeddings for Bias Disparity Reduction in Collaborative Filtering.
  Hiroki Okamura; Keisuke Maeda; Ren Togo; Takahiro Ogawa 0001; Miki Haseyama
  IEEE Trans. Serv. Comput., 18, 4, 2201, 2211, 2025, [Peer-reviewed], [International Magazine]
  Scientific journal
• Cross-domain multi-step thinking: Zero-shot fine-grained traffic sign recognition in the wild.
  Yaozong Gan; Guang Li 0008; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  Knowl. Based Syst., 327, 114172, 114172, 2025, [Peer-reviewed], [International Magazine]
  Scientific journal
• Generative Dataset Distillation Based on Large Model Pool
  Longzhen Li; Guang Li; Ren Togo; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  2024 IEEE 13th Global Conference on Consumer Electronics (GCCE), 458, 459, IEEE, 29 Oct. 2024, [Peer-reviewed], [International Magazine]
  International conference proceedings
• Snow- or ice-covered road detection in winter road surface conditions using deep neural networks.
  Yuya Moroto; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  Computer-Aided Civil and Infrastructure Engineering, 39, 19, 2935, 2950, Oct. 2024, [Peer-reviewed], [International Magazine]
  Scientific journal
• Multimodal Shot Prediction Based on Spatial-Temporal Interaction between Players in Soccer Videos
  Ryota Goka; Yuya Moroto; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  Applied Sciences, 03 Jun. 2024, [Peer-reviewed], [International Magazine]
  Scientific journal
• Multimodal Transformer Model Using Time-Series Data to Classify Winter Road Surface Conditions.
  Yuya Moroto; Keisuke Maeda; Ren Togo; Takahiro Ogawa 0001; Miki Haseyama
  Sensors, 24, 11, 3440, 3440, Jun. 2024, [Peer-reviewed], [International Magazine]
  Scientific journal
• Trial Analysis of Brain Activity Information for the Presymptomatic Disease Detection of Rheumatoid Arthritis.
  Keisuke Maeda; Takahiro Ogawa; Tasuku Kayama; Takuya Sasaki; Kazuki Tainaka; Masaaki Murakami; Miki Haseyama
  Bioengineering (Basel, Switzerland), 11, 6, 21 May 2024, [Peer-reviewed], [Lead author], [International Magazine]
  English, Scientific journal, This study presents a trial analysis that uses brain activity information obtained from mice to detect rheumatoid arthritis (RA) in its presymptomatic stages. Specifically, we confirmed that F759 mice, serving as a mouse model of RA that is dependent on the inflammatory cytokine IL-6, and healthy wild-type mice can be classified on the basis of brain activity information. We clarified which brain regions are useful for the presymptomatic detection of RA. We introduced a matrix completion-based approach to handle missing brain activity information to perform the aforementioned analysis. In addition, we implemented a canonical correlation-based method capable of analyzing the relationship between various types of brain activity information. This method allowed us to accurately classify F759 and wild-type mice, thereby identifying essential features, including crucial brain regions, for the presymptomatic detection of RA. Our experiment obtained brain activity information from 15 F759 and 10 wild-type mice and analyzed the acquired data. By employing four types of classifiers, our experimental results show that the thalamus and periaqueductal gray are effective for the classification task. Furthermore, we confirmed that classification performance was maximized when seven brain regions were used, excluding the electromyogram and nucleus accumbens.
• Generalizing deep learning-based distress segmentation models for subway tunnel images by test-time training
  Zongyao Li; Keisuke Maeda; Ren Togo; Takahiro Ogawa; Miki Haseyama
  Intelligence, Informatics and Infrastructure, 5, 1, 34, 41, May 2024, [Peer-reviewed], [International Magazine]
  Scientific journal
• Analysis of Continual Learning Techniques for Image Generative Models with Learned Class Information Management.
  Taro Togo; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  Sensors, 24, 10, 3087, 3087, May 2024, [Peer-reviewed], [International Magazine]
  Scientific journal
• A Novel Frame-Selection Metric for Video Inpainting to Enhance Urban Feature Extraction.
  Yuhu Feng; Jiahuan Zhang; Guang Li 0008; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  Sensors, 24, 10, 3035, 3035, May 2024, [Peer-reviewed], [International Magazine]
  Scientific journal
• Algal Bed Region Segmentation Based on a ViT Adapter Using Aerial Images for Estimating CO2 Absorption Capacity.
  Guang Li 0008; Ren Togo; Keisuke Maeda; Akinori Sako; Isao Yamauchi; Tetsuya Hayakawa; Shigeyuki Nakamae; Takahiro Ogawa 0001; Miki Haseyama
  Remote. Sens., 16, 10, 1742, 1742, May 2024, [Peer-reviewed], [International Magazine]
  Scientific journal
• Flexibly manipulating popularity bias for tackling trade-offs in recommendation.
  Hiroki Okamura; Keisuke Maeda; Ren Togo; Takahiro Ogawa 0001; Miki Haseyama
  Inf. Process. Manag., 61, 2, 103606, 103606, Mar. 2024, [Peer-reviewed], [International Magazine]
  Scientific journal
• Text-Guided Image Editing Based on Post Score for Gaining Attention on Social Media.
  Yuto Watanabe; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  Sensors, 24, 3, 921, 921, Feb. 2024, [Peer-reviewed], [International Magazine]
  Scientific journal
• Masked Modeling-based Action Event Prediction Considering Bidirectional Time-series in Soccer
  Ryota Goka; Yuya Moroto; Keisuke Maeda; Takahiro Ogawa; Huang-Chia Shih; Miki Haseyama
  INTERNATIONAL WORKSHOP ON ADVANCED IMAGING TECHNOLOGY, IWAIT 2024, 13164, 2024
  English, International conference proceedings
• Zero-Shot Visual Sentiment Prediction via Cross-Domain Knowledge Distillation
  Yuya Moroto; Yingrui Ye; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  IEEE Open Journal of Signal Processing, 2024
  Scientific journal
• Automatic generation of findings using generative AI to support for inspection report creation -Introduction of in-context learning based on similar image retrieval using data pool compression-
  佐藤雅也; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀
  AI・データサイエンス論文集(Web), 5, 3, 2024, [Peer-reviewed], [Domestic magazines]
• Hyperboloid GPLVM for Discovering Continuous Hierarchies via Nonparametric Estimation.
  Koshi Watanabe; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  CoRR, abs/2410.16698, 2024
  Scientific journal
• Think Twice Before Recognizing: Large Multimodal Models for General Fine-grained Traffic Sign Recognition.
  Yaozong Gan; Guang Li 0008; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  CoRR, abs/2409.01534, 2024
  Scientific journal
• Generalizing Human Motion Style Transfer Method Based on Metadata-independent Learning.
  Yuki Era; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  SIGGRAPH Asia Posters, 78, 3, 2024, [Peer-reviewed], [International Magazine]
  International conference proceedings
• An Evaluation Metric for Single Image-to-3D Models Based on Object Detection Perspective.
  Yuiko Uchida; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  SIGGRAPH Asia 2024 Technical Communications, 31, 4, ACM, 2024, [Peer-reviewed], [International Magazine]
  International conference proceedings
• What to Do and Where to Go Next? Action Prediction in Soccer Using Multimodal Co-Attention Transformer.
  Ryota Goka; Yuya Moroto; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  Proceedings of the 7th ACM International Workshop on Multimedia Content Analysis in Sports(MMSports@MM), 75, 79, ACM, 2024, [Peer-reviewed], [International Magazine]
  International conference proceedings
• Introducing Class Replacement Technique in Class Incremental Learning in Generative Models.
  Taro Togo; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  International Conference on Consumer Electronics - Taiwan(ICCE-Taiwan), 457, 458, IEEE, 2024, [Peer-reviewed], [International Magazine]
  International conference proceedings
• Motion-STUDiO : Motion Style Transfer Utilized for Dancing Operation by Considering Both Style and Dance Features.
  Yuki Era; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  International Conference on Consumer Electronics - Taiwan(ICCE-Taiwan), 127, 128, IEEE, 2024, [Peer-reviewed], [International Magazine]
  International conference proceedings
• Privacy Preserving Gaze Estimation Via Federated Learning Adapted To Egocentric Video.
  Yuhu Feng; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP), 3500, 3504, IEEE, 2024, [Peer-reviewed], [International Magazine]
  International conference proceedings
• Multimodal Adversarial Defense Trained on Features Extracted from Images and Brain Activity.
  Tasuku Nakajima; Keisuke Maeda; Ren Togo; Takahiro Ogawa 0001; Miki Haseyama
  GCCE, 1183, 1184, 2024, [Peer-reviewed], [International Magazine]
  International conference proceedings
• Zero-shot Controllable Music Generation from Videos Using Facial Expressions.
  Shilin Liu; Kyohei Kamikawa; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  GCCE, 1169, 1170, 2024, [Peer-reviewed], [International Magazine]
  International conference proceedings
• Personalized Visual Emotion Classification via In-context Learning in Multimodal LLM.
  Ryo Takahashi; Naoki Saito 0006; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  GCCE, 1167, 1168, 2024, [Peer-reviewed], [International Magazine]
  International conference proceedings
• Zero-shot Composed Video Retrieval with Projection Module Bridging Modality Gap.
  Kenta Uesugi; Naoki Saito 0006; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  GCCE, 6, 7, 2024, [Peer-reviewed], [International Magazine]
  International conference proceedings
• Multi-Modal Gaussian Process Latent Variable Model With Semi-Supervised Label Dequantization.
  Keisuke Maeda; Masanao Matsumoto; Naoki Saito 0006; Takahiro Ogawa 0001; Miki Haseyama
  IEEE Access, 12, 127244, 127258, 2024, [Peer-reviewed], [International Magazine]
  Scientific journal
• MLLM-based Automatic Exploration of Editing Prompt for High Engagement Image Generation.
  Kenta Kubota; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  GCCE, 1165, 1166, 2024, [Peer-reviewed], [International Magazine]
  International conference proceedings
• Improving Zero-shot Adversarial Robustness via Integrating Image Features of Foundation Models.
  Koshiro Toishi; Keisuke Maeda; Ren Togo; Takahiro Ogawa 0001; Miki Haseyama
  GCCE, 148, 149, 2024, [Peer-reviewed], [International Magazine]
  International conference proceedings
• Emotion-conditional Image Generation Reflecting Semantic Alignment with Text-to-Image Models.
  Kaede Hayakawa; Keisuke Maeda; Ren Togo; Takahiro Ogawa 0001; Miki Haseyama
  GCCE, 10, 11, 2024, [Peer-reviewed], [International Magazine]
  International conference proceedings
• AUTOMATIC RECOGNITION OF ALGAL BED AREAS BASED ON A LARGE-SCALE SEMANTIC SEGMENTATION MODEL FOR ESTIMATING CO₂ ABSORPTION BY BLUE CARBON
  LI Guang; TOGO Ren; MAEDA Keisuke; SAKO Akinori; YAMAUCHI Isao; HAYAKAWA Tetsuya; NAKAMAE Shigeyuki; OGAWA Takahiro; HASEYAMA Miki
  Japanese Journal of JSCE, 80, 17, n/a, Japan Society of Civil Engineers, 2024, [Peer-reviewed], [Domestic magazines]
  Japanese, Measuring the CO₂ absorption of algal beds is one of the key issues for achieving carbon neutrality, but identifying the area of algal beds from UAV images requires a great deal of labor and experience. In this study, we propose a method for automatic recognition of algal beds using UAV images. The proposed method uses a model that enables semantic domain segmentation at the pixel level, and employs ViT-Adapter, one of the latest models. The advantage of this technique is that it effectively utilizes the knowledge of a trained large-scale model to recognize algal beds, and it can identify algal beds at the pixel level by adjusting the parameters of the model. In this study, we conducted learning using mask images of visually identified algal beds from aerial photographs, and further examined data expansion and other processing to adapt the learning to UAV images. The effectiveness of this method was verified through a demonstration using UAV images of the Erimo coast of Hokkaido.
• Generative Dataset Distillation: Balancing Global Structure and Local Details.
  Longzhen Li; Guang Li 0008; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  CVPR Workshops, 7664, 7671, IEEE, 2024, [Peer-reviewed], [International Magazine]
  International conference proceedings
• Cross-domain Few-shot In-context Learning for Enhancing Traffic Sign Recognition.
  Yaozong Gan; Guang Li 0008; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  CoRR, abs/2407.05814, 2564, 2570, 2024
  Scientific journal
• Multi-Object Editing in Personalized Text-To-Image Diffusion Model Via Segmentation Guidance.
  Haruka Matsuda; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  ICASSP, 8140, 8144, IEEE, 2024, [Peer-reviewed], [International Magazine]
  International conference proceedings
• Caption Unification for Multi-View Lifelogging Images Based on In-Context Learning with Heterogeneous Semantic Contents.
  Masaya Sato; Keisuke Maeda; Ren Togo; Takahiro Ogawa 0001; Miki Haseyama
  ICASSP, 8085, 8089, IEEE, 2024, [Peer-reviewed], [International Magazine]
  International conference proceedings
• Enhancing Noisy Label Learning Via Unsupervised Contrastive Loss with Label Correction Based on Prior Knowledge.
  Masaki Kashiwagi; Keisuke Maeda; Ren Togo; Takahiro Ogawa 0001; Miki Haseyama
  ICASSP, 6235, 6239, IEEE, 2024, [Peer-reviewed], [International Magazine]
  International conference proceedings
• Expert-novice level classification using engineers’ motion data in subway tunnel inspections - Introduction of explainable graph convolutional network -
  SEINO Tatsuki; SAITO Naoki; MAEDA Keisuke; OGAWA Takahiro; HASEYAMA Miki
  Artificial Intelligence and Data Science, 5, 1, 101, 109, Japan Society of Civil Engineers, 2024, [Peer-reviewed]
  Japanese, Skill transfer to young engineers from senior engineers is a very important task in infrastructure equipment inspection. To support the skill transfer, an analysis method of the key factors of senior engineers skill is needed. However, conventional research has been limited to skill-level classification or analysis of the relationship between the skill level and biological data such as eye gaze and motion obtained from the engineers. This paper presents a method of classifying the skill level and visualization of its key factors to support the skill transfer. The proposed method employs a graph convolutional network introducing a novel attention mechanism for the classification and visualization.
• Reinforcing Pre-trained Models Using Counterfactual Images.
  Xiang Li; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  CoRR, abs/2406.13316, 486, 492, 2024
  Scientific journal
• Generative Dataset Distillation: Balancing Global Structure and Local Details.
  Longzhen Li; Guang Li 0008; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  CoRR, abs/2404.17732, 2024
  Scientific journal
• Enhancing Generative Class Incremental Learning Performance with Model Forgetting Approach.
  Taro Togo; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  CoRR, abs/2403.18258, 2024, [Peer-reviewed]
  Scientific journal
• Automatic Findings Generation for Distress Images Using In-Context Few-Shot Learning of Visual Language Model Based on Image Similarity and Text Diversity.
  Yuto Watanabe; Naoki Ogawa; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  J. Robotics Mechatronics, 36, 2, 353, 364, 2024, [Peer-reviewed], [International Magazine]
  Scientific journal
• Individual Persistence Adaptation for User-Centric Evaluation of User Satisfaction in Recommender Systems.
  Nozomu Onodera; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  IEEE Access, 12, 23626, 23635, 2024, [Peer-reviewed], [International Magazine]
  Scientific journal
• Estimation of contact accident risk based on recurrent neural network introducing spatial-temporal attention
  五箇亮太; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀
  AI・データサイエンス論文集(Web), 5, 1, 117, 125, Japan Society of Civil Engineers, 2024, [Peer-reviewed]
  Japanese, In this paper, we propose a method for estimating the contact accidents risk with heavy machinery to support safety management on construction sites. According to recent reports on occupational accidents, since the construction industry experiences a high number of incidents, preventing contact accidents between heavy machinery and workers, which are on the increase, is a crucial task. The proposed method constructs a deep learning model to estimate the contact accident risk by using videos obtained from multiple viewpoints of cameras mounted on heavy machinery or fixed-point cameras at a construction site. By inputting visual information of videos obtained via spatial-temporal attention to a recurrent neural network, it is possible to accurately estimate the risk of contact accidents. At the end of this paper, we can verify the effectiveness of the proposed method through experiments using videos taken at actual construction sites.
• Zero-shot high-risk situation detection based on object detection and pose estimation using fixed camera at construction site
  大羽賀駿也; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀
  AI・データサイエンス論文集(Web), 5, 1, 110, 116, Japan Society of Civil Engineers, 2024, [Peer-reviewed]
  Japanese, In this study, we propose a zero-shot high-risk situation detection method based on object detection and pose estimation using a fixed camera, aimed at preventing labor accidents at construction sites. By utilizing pre-trained object detection models and pose estimation models, our proposed method determines two high-risk situations: when a worker is unaware of an approaching vehicle, and when a worker is in the roadway. Our proposed method enables the detection of high-risk situations and is expected to enhance safety awareness among workers and managers by providing statistical information such as the time of occurrence and frequency of the detected high-risk situations. At the end of this paper, we verify the effectiveness of the proposed method through experiments using the videos taken at the entrance and exit of a construction site.
• Prediction of event locations from urgent call using large language models
  吉田将規; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀
  AI・データサイエンス論文集(Web), 5, 1, 33, 42, Japan Society of Civil Engineers, 2024, [Peer-reviewed]
  Japanese, In this study, we propose a method to predict locations of road-related events from urgent call data by using large language models. Operators need to identify the location of road-related events from information verbally conveyed by the reporter during the call, and this requires them to have both job experience and geographical knowledge. We aim to construct the framework that predicts the location from urgent calls to alleviate the burden on operators in their operations. Thus, we utilize the large language models with extensive pre-training knowledge to extract location information from the text transcribed using a speech recognition model. We evaluate the proposed method using real urgent call data to assess its effectiveness and highlight the remaining problems of this study.
• Zero-Shot Traffic Sign Recognition Based on Midlevel Feature Matching
  Yaozong Gan; Guang Li; Ren Togo; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  Sensors, 23, 23, 9607, 9607, MDPI AG, 04 Dec. 2023
  Scientific journal, Traffic sign recognition is a complex and challenging yet popular problem that can assist drivers on the road and reduce traffic accidents. Most existing methods for traffic sign recognition use convolutional neural networks (CNNs) and can achieve high recognition accuracy. However, these methods first require a large number of carefully crafted traffic sign datasets for the training process. Moreover, since traffic signs differ in each country and there is a variety of traffic signs, these methods need to be fine-tuned when recognizing new traffic sign categories. To address these issues, we propose a traffic sign matching method for zero-shot recognition. Our proposed method can perform traffic sign recognition without training data by directly matching the similarity of target and template traffic sign images. Our method uses the midlevel features of CNNs to obtain robust feature representations of traffic signs without additional training or fine-tuning. We discovered that midlevel features improve the accuracy of zero-shot traffic sign recognition. The proposed method achieves promising recognition results on the German Traffic Sign Recognition Benchmark open dataset and a real-world dataset taken from Sapporo City, Japan.
• Manipulation Direction: Evaluating Text-Guided Image Manipulation Based on Similarity between Changes in Image and Text Modalities.
  Yuto Watanabe; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  Sensors, 23, 22, 9287, 9287, Nov. 2023
  Scientific journal
• Visual Emotion Recognition Through Multimodal Cyclic-Label Dequantized Gaussian Process Latent Variable Model.
  Naoki Saito 0006; Keisuke Maeda; Takahiro Ogawa 0001; Satoshi Asamizu; Miki Haseyama
  Journal of Robotics and Mechatronics, 35, 5, 1321, 1330, Oct. 2023
  Scientific journal
• Zero-Shot Neural Decoding with Semi-Supervised Multi-View Embedding.
  Yusuke Akamatsu; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  Sensors, 23, 15, 6903, 6903, Aug. 2023
  Scientific journal
• A Gaussian Process Decoder with Spectral Mixtures and a Locally Estimated Manifold for Data Visualization
  Koshi Watanabe; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  Applied Sciences, 09 Jul. 2023
  Scientific journal
• Canonical Correlation Analysis Introducing Label Dequantization for Visual Emotion Recognition
  SAITO Naoki; MAEDA Keisuke; OGAWA Takahiro; ASAMIZU Satoshi; HASEYAMA Miki
  電子情報通信学会論文誌D 情報・システム, J106-D, 5, 337, 348, The Institute of Electronics, Information and Communication Engineers, 01 May 2023
  Japanese, Supervised multi-view canonical correlation analysis via cyclic label dequantization (sMVCCA-CLD) for visual emotion recognition is presented in this paper. In the CCA approach, the dimension of latent common space is limited to the minimum dimension among those of all features. The dimension of label features i.e., the number of classes for label information, tends to be lower than those of the other features. Then the dimension of the latent common space constructed by CCA becomes lower. Therefore, there is a possibility of misssing important information that is necessary for the estimation from the latent common space due to the dimensionality constraint. To overcome this constraint, sMVCCA-CLD increases the dimension of the label features by the label dequantization process, and estimates the canonical correlation between multi-view features. In addition, sMVCCA-CLD performs the label dequantization considering that the emotions are represented by a cyclic model, e.g., Plutchik's and Mikel's wheels. Consequently, the construction of the latent common space for the accurate recognition of emotions becomes feasible.
• Estimation of Degradation Degree in Road Infrastructure Based on Multi-Modal ABN Using Contrastive Learning.
  Takaaki Higashi; Naoki Ogawa; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  Sensors, 23, 3, 1657, 1657, Feb. 2023
  Scientific journal
• TolerantGAN: Text-guided Image Manipulation Tolerant to Real-world Image
  Yuto Watanabe; Ren Togo; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  IEEE Open Journal of Signal Processing, 2023
  Scientific journal
• Automatic generation of findings for distress images using visual language model—Introduction of few-shot learning based on similar image retrieval—
  WATANABE Yuto; OGAWA Naoki; MAEDA Keisuke; OGAWA Takahiro; HASEYAMA Miki
  Artificial Intelligence and Data Science, 4, 3, 223, 232, Japan Society of Civil Engineers, 2023, [Peer-reviewed]
  Japanese, In this study, we propose a novel method for automatic generation of findings using a visual language model to support the efficient creation of findings in inspection records for infrastructure facilities. It is essential for the creation of inspection records to write findings, which are sentences that include judgments and opinions of engineers in addition to what can be recognized from the distress image. However, there has been little discussion on the direct automatic generation of findings, and it is expected to realize generation methods to support the efficient creation of findings. With this background, in this paper, we introduce few-shot learning based on the similarity of distress images to the visual language model, which is an application of large language models attracted much attention in recent years and enables text output with a highly accurate understanding of both vision and language. By using past inspection records including images similar to the distress images, we can efficiently consider the relationship between the distress images and findings from a small number of pairs of them. In the last part of this paper, we confirm the effectiveness of the proposed method through experiments generating findings from the distress images included in the inspection records of bridges.
• Distress estimation of road attachments based on attention-based multiple instance learning considering the diversity of background of images
  WATANABE Koshi; OGAWA Naoki; MAEDA Keisuke; OGAWA Takahiro; HASEYAMA Miki
  Artificial Intelligence and Data Science, 4, 3, 482, 489, Japan Society of Civil Engineers, 2023, [Peer-reviewed]
  Japanese, In this paper, we propose a distress estimation method for road attachments. Road attachments, such as signs and lighting, are equipped over a huge number and wide area, and therefore it is desired to achieve automatic inspection by using drones to reduce the burden on the inspectors. While captured images by drones include the diversity of the background including ground, sky, and road surfaces, the previous methods did not consider the diversity of the background of captured images of road attachments. This study proposes the distress estimation method via attention-based multiple instance learning to address this issue. We input patches of the images into the estimation model to distinguish between the background area and road attachment area and assign importance weight, or attention, to each patch. By performing this strategy, we realize the distress estimation method considering the diversity of the background area of images. In the experiment, we achieve a classification accuracy of about 70 % using images of actual road attachments confirming the effectiveness of this research approach.
• Multi-task classification of distress types and deterioration levels for infrastructure maintenance
  OGAWA Naoki; MAEDA Keisuke; OGAWA Takahiro; HASEYAMA Miki
  Artificial Intelligence and Data Science, 4, 3, 807, 814, Japan Society of Civil Engineers, 2023, [Peer-reviewed]
  Japanese, This paper proposes a multi-task classification method that classifies both the distress type and deterioration level at the same time. Conventionally, classifying the deterioration level has been conducted for each distress type by using multiple models. In contrast, the proposed method enables classification of the deterioration level without assigning a distress type to the distress image in advance, by training a single model through loss minimization considering the distress type and deterioration level. In the last part of the paper, it is verified that the proposed method can achieve classification performance equivalent to models constructed for each distress type with a single model by using images of actual distress that have occurred on infrastructure.
• Advanced AI research for enhancing the efficiency of infrastructure maintenance and management
  MAEDA Keisuke; OGAWA Takahiro; HASEYAMA Miki
  Artificial Intelligence and Data Science, 4, 3, 982, 989, Japan Society of Civil Engineers, 2023, [Peer-reviewed]
  Japanese, With the development and advancement of AI technology, research on the application to the field of infrastructure maintenance is actively progressing. Many of these studies focus on the development of learning theories that consider the characteristics of images obtained in infrastructure maintenance. The effectiveness of AI has been demonstrated in various tasks such as crack detection, classification of defect types, and estimation of degradation levels. On the other hand, to truly enhance the operational efficiency through AI, it is necessary to construct AI systems considering the practical business. Furthermore, to improve AI and continuously utilize AI, it is necessary to acquire images suitable for AI development in operations. Therefore, this paper introduces the learning theories that have been developed for images obtained in infrastructure maintenance, previous research on AI with essential function for the practical business and the authors’ idea on efficient image acquisition.
• Automatic detection of dead trees using in-vehicle video based on semantic segmentation
  OGAWA Naoki; MAEDA Keisuke; OGAWA Takahiro; HASEYAMA Miki
  Artificial Intelligence and Data Science, 4, 3, 686, 693, Japan Society of Civil Engineers, 2023, [Peer-reviewed]
  Japanese, This paper proposes a method for automatic detection of dead trees using in-vehicle videos. The proposed method extracts vegetation regions from videos containing various objects based on semantic segmentation. Then, it detects dead trees from the extracted vegetation regions using color information. By presenting the dead tree regions detected by the proposed method to engineers, they can find dead trees efficiently. In the last part of this paper, the effectiveness of the proposed method is verified through experiments using actual in-vehicle videos.
• Feature Integration via Back-Projection Ordering Multi-Modal Gaussian Process Latent Variable Model for Rating Prediction.
  Kyohei Kamikawa; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  ICIP, 3125, 3129, IEEE, 2023
  International conference proceedings
• Multi-View Variational Recurrent Neural Network for Human Emotion Recognition Using Multi-Modal Biological Signals.
  Yuya Moroto; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  ICIP, 2925, 2929, IEEE, 2023
  International conference proceedings
• Video-Music Retrieval with Fine-Grained Cross-Modal Alignment.
  Yuki Era; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  ICIP, 2005, 2009, IEEE, 2023
  International conference proceedings
• Text-Guided Facial Image Manipulation for Wild Images via Manipulation Direction-Based Loss.
  Yuto Watanabe; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  ICIP, 361, 365, IEEE, 2023
  International conference proceedings
• Text-to-image Diffusion Model Suppressing Catastrophic Forgetting via Elastic Weight Consolidation.
  Haruka Matsuda; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  GCCE, 831, 832, IEEE, 2023
  International conference proceedings
• Deterioration Level Estimation for Infrastructures Considering Noisy Labels via DivideMix.
  Masaki Kashiwagi; Keisuke Maeda; Ren Togo; Takahiro Ogawa 0001; Miki Haseyama
  GCCE, 829, 830, IEEE, 2023
  International conference proceedings
• Novel Feature Extraction for Classification of Auditory-visual Stimuli from fNIRS Signals.
  Taro Togo; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  GCCE, 759, 760, IEEE, 2023
  International conference proceedings
• A Controllable Recoloring Method for Novel Views Using Segment Anything Model.
  Haoyang Wang; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  GCCE, 587, 588, IEEE, 2023
  International conference proceedings
• Caption Unification for Multiple Viewpoint Lifelogging Images and Its Verification.
  Masaya Sato; Keisuke Maeda; Ren Togo; Takahiro Ogawa 0001; Miki Haseyama
  GCCE, 415, 416, IEEE, 2023
  International conference proceedings
• Improving Visual Counterfactual Explanation Models for Image Classification via CLIP.
  Xiang Li; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  GCCE, 390, 391, IEEE, 2023
  International conference proceedings
• Acquisition of feature representation of record data via graph neural network to support determination of deterioration levels
  山本一輝; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀
  AI・データサイエンス論文集(Web), 4, 3, 694, 704, Japan Society of Civil Engineers, 2023, [Peer-reviewed]
  Japanese, In this paper, we propose a method of acquiring feature representations of record data via the graph neural network to assist in determining the deterioration levels. In the inspection work, multiple deformation images are captured from different angles and distances and stored as record data. However, conventional studies on the deterioration level classification assume the input of a single image for model learning. This makes it difficult to handle the input of record data that has different properties from those of a single image. Therefore, in this paper, to deal with record data, which is a group of multiple images, we construct a graph neural network that can learn the relationship between the individual images and the record data. Therefore, we can acquire feature representations of the record data. In the last part of the paper, the effectiveness of the proposed method is verified through experiments using deformation images obtained during actual inspections.
• Distress detection using egocentric videos for increasing discovery rate of novel distress during subway tunnel inspection
  櫻井慶悟; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀
  AI・データサイエンス論文集(Web), 4, 3, 393, 401, Japan Society of Civil Engineers, 2023, [Peer-reviewed]
  Japanese, In this paper, we propose a novel distress detection method using egocentric videos with the aim of increasing the discovery rate of novel distresses by novice engineers who fail to recognize them despite observing potential distress areas. In the proposed method, we introduce a mechanism that outputs an attention map that emphasizes potential distress areas into the deep learning model, which determines the presence or absence of distress from frames of egocentric videos taken by novice engineers during inspections. The proposed method enables high-precision distress detection and provides a basis for determining the results of detection. We confirm the effectiveness of our method through experiments using egocentric videos taken by actual the subway tunnel engineers.
• Classification of Winter Road Surface Condition Based on Multi-modal Transformer Using Sequential Data
  諸戸祐哉; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀
  AI・データサイエンス論文集(Web), 4, 3, 402, 413, Japan Society of Civil Engineers, 2023, [Peer-reviewed]
  Japanese, This paper proposes a Multi-modal transformer using sequential data for detecting and predicting the deterioration of winter road surface conditions caused by snow accumulation. The proposed method performs multimodal analysis using multiple modalities including images captured by a fixed-point camera and text data related to road surface conditions. When integrating these multiple modalities, we adopt the feature integration based on cross attention for compensating features based on complementation among multiple modalities, and improvement of the expressive power of the integrated features can be achieved. Besides, by applying time-series processing for input data at multiple times, the temporal changes in road surface conditions are considered. At the end of this paper, in otder to verify the effectiveness of the proposed method for both detection and prediction tasks, the experiments are conducted using the road surface conditions corresponding to the input data and the road surface conditions several hours after the input data as the supervised data.
• DEGRADATION DEGREE ESTIMATION OF DISTRESS IMAGE IN ROAD INFRASTRUCTURE USING MULTI-DATASET CONTRASTIVE LEARNING
  東孝明; 小川直輝; 前田圭介; 小川貴弘; 長谷山美紀
  AI・データサイエンス論文集(Web), 4, 2, 44, 57, Japan Society of Civil Engineers, 2023, [Peer-reviewed]
  Japanese, For training a deep learning model that can estimate the degree of degradation from distress images in road infrastructures, pairs of distress images and their degradation degrees as labels are needed. Although a large number of pairs is desirable for achieving high estimation performance, the total number of such pairs is limited. On the other hand, there is an open dataset composed of distress images in other infrastructures. It is expected to improve the estimation performance of degradation degrees by using distress images of the open dataset in addition to the distress images of the road infrastructure dataset. However, in the open dataset, the distress images are not annotated with labels indicating the degradation degrees. Therefore, we propose a method for estimating degradation degrees across multiple datasets by introducing contrastive learning, regardless independent of the presence or absence of labels. The multi-dataset contrastive learning is performed as a pre-task of supervised learning. The obtained model parameters are used in supervised learning to estimate the degradation degrees of distress images in road infrastructures, and it is possible to achieve the improvement of estimation performance. The effectiveness of the proposed method is verified through experiments using real-world distress images in road infrastructure and an open dataset.
• Few-Shot Personalized Saliency Prediction Using Tensor Regression for Preserving Structural Global Information.
  Yuya Moroto; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  CoRR, abs/2307.02799, 2023
  Scientific journal
• Personalized Content Recommender System via Non-verbal Interaction Using Face Mesh and Facial Expression.
  Yuya Moroto; Rintaro Yanagi; Naoki Ogawa; Kyohei Kamikawa; Keigo Sakurai; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  ACM Multimedia, 9399, 9401, ACM, 2023
  International conference proceedings
• Prediction of Shoot Events by Considering Spatio-temporal Relations of Multimodal Features.
  Ryota Goka; Yuya Moroto; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  ICCE-Taiwan, 793, 794, IEEE, 2023
  International conference proceedings
• Shoot Event Prediction in Soccer Considering Expected Goals Based on Players' Positions.
  Ryota Goka; Yuya Moroto; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  ICCE-Taiwan, 449, 450, IEEE, 2023
  International conference proceedings
• Estimation of Amyloid-β Positivity Using QSM Images Considering Age Information.
  Tsubasa Kunieda; Ren Togo; Noriko Nishioka; Yukie Shimizu; Shiro Watanabe; Kenji Hirata; Keisuke Maeda; Takahiro Ogawa 0001; Kohsuke Kudo; Miki Haseyama
  ICCE-Taiwan, 165, 166, IEEE, 2023
  International conference proceedings
• Defense Against Black-Box Adversarial Attacks Via Heterogeneous Fusion Features.
  Jiahuan Zhang; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  ICASSP, 1, 5, IEEE, 2023
  International conference proceedings
• Learning Graph Laplacian from Intrinsic Patterns via Gaussian Process.
  Koshi Watanabe; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  ICASSP, 1, 5, IEEE, 2023
  International conference proceedings
• Estimation of Visual Contents from Human Brain Signals via VQA Based on Brain-Specific Attention.
  Ryo Shichida; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  ICASSP, 1, 5, IEEE, 2023
  International conference proceedings
• Improving Dropout in Graph Convolutional Networks for Recommendation via Contrastive Loss.
  Hiroki Okamura; Keisuke Maeda; Ren Togo; Takahiro Ogawa 0001; Miki Haseyama
  ICASSP, 1, 5, IEEE, 2023
  International conference proceedings
• Multi-Label Classification in Anime Illustrations Based on Hierarchical Attribute Relationships.
  Ziwen Lan; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  Sensors, 23, 10, 4798, 4798, 2023
  Scientific journal
• Prediction of Shooting Events in Soccer Videos Using Complete Bipartite Graphs and Players' Spatial-Temporal Relations.
  Ryota Goka; Yuya Moroto; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  Sensors, 23, 9, 4506, 4506, 2023
  Scientific journal
• Material Compound-Property Retrieval Using Electron Microscope Images for Rubber Material Development.
  Rintaro Yanagi; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  IEEE Access, 11, 88258, 88264, 2023
  Scientific journal
• Hierarchical Multi-Label Attribute Classification With Graph Convolutional Networks on Anime Illustration.
  Ziwen Lan; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  IEEE Access, 11, 35447, 35456, 2023
  Scientific journal
• SpectralMAP: Approximating Data Manifold With Spectral Decomposition
  Koshi Watanabe; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  IEEE ACCESS, 11, 31530, 31540, 2023
  English, Scientific journal
• Summarizing Data Structures with Gaussian Process and Robust Neighborhood Preservation
  Koshi Watanabe; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT V, 13717, 157, 173, 2023
  English, International conference proceedings
• Text-Guided Image Manipulation via Generative Adversarial Network With Referring Image Segmentation-Based Guidance.
  Yuto Watanabe; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  IEEE Access, 11, 42534, 42545, 2023
  Scientific journal
• Brain Decoding of Multiple Subjects for Estimating Visual Information Based on a Probabilistic Generative Model
  Takaaki Higashi; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  SENSORS, 22, 16, 6148, 6148, Aug. 2022
  English, Scientific journal
• Chain centre loss: A psychology inspired loss function for image sentiment analysis
  Yun Liang; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  NEUROCOMPUTING, 495, 118, 128, Jul. 2022, [Peer-reviewed]
  English, Scientific journal
• Regularization Meets Enhanced Multi-Stage Fusion Features: Making CNN More Robust against White-Box Adversarial Attacks
  Jiahuan Zhang; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  SENSORS, 22, 14, 5431, 5431, Jul. 2022, [Peer-reviewed]
  English, Scientific journal
• Development of Spin Stabilization Control System for the Cosmic Dust Observation CubeSat.
  Shinya Fujita 0002; Ryo Ishimaru; Yuji Sakamoto; Keisuke Maeda; Osamu Okudaira; Yuji Sato; Toshinori Kuwahara; Takafumi Matsui
  IEEE/SICE International Symposium on System Integration(SII), 114, 119, IEEE, 2022
  International conference proceedings
• DETERIORATION LEVEL CLASSIFICATION OF DISTRESS IMAGE CONSIDERING CORRELATION AMONG HETEROGENEOUS FEATURES AND ATTENTION MAP CONFIDENCE
  小川直輝; 前田圭介; 小川貴弘; 長谷山美紀
  AI・データサイエンス論文集(Web), 3, J2, 704, 713, Japan Society of Civil Engineers, 2022, [Peer-reviewed]
  Japanese, To support the maintenance of infrastructure structures, research on deep learning to classify the progression of distress from images has been widely conducted. In the classification of distress images, deep learning models are likely to incorrectly focus on regions unrelated to the classification target due to the complexity of the real data, such as the diversity of the subjects and the distance between the camera and the subject. Therefore, in this paper, we construct a multi-modal deep learning model that can focus on regions of distress by introducing text data indicating the parts and materials where distress occurs to the conventional deep learning model that uses only images. Furthermore, by calculating the confidence which indicates how confident the model is in the focused distress regions, the effect of the focused regions with high confidence on the distress classification can be controlled, and thus, the classification performance is improved.
• WINTER ROAD SURFACE CONDITION CLASSIFICATION USING DEEP LEARNING WITH FOCAL LOSS BASED ON TEXT AND IMAGE INFORMATION
  諸戸祐哉; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀
  AI・データサイエンス論文集(Web), 3, J2, 293, 306, Japan Society of Civil Engineers, 2022, [Peer-reviewed]
  Japanese, This paper presents a winter road surface condition classification method using deep learning with focal loss based on text and image information for detecting the deterioration of road surface conditions caused by snow accumulation. The proposed method achieves multimodal road surface condition classification by constructing a deep learning model that can cooperatively use images automatically captured by fixed-point cameras installed along the road surface, and text data related to road surface conditions. Since the distribution of training data is biased toward winter road surface conditions, there is a concern that the classification accuracy may be degraded due to the data imbalance problem. Therefore, the proposed method uses focal loss, which can deal with data imbalance, to train a deep learning model to realize road surface condition classification considering data imbalance. In the end of this paper, we demonstrate the effectiveness of the proposed method by conducting experiments using real data.
• DISTRESS DETECTION BASED ON VISION TRANSFORMER USING EGOCENTRIC VIDEOS WHILE INSPECTING IN SUBWEY TUNNELS
  櫻井慶悟; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀
  AI・データサイエンス論文集(Web), 3, J2, 470, 478, Japan Society of Civil Engineers, 2022, [Peer-reviewed]
  Japanese, In this paper, we propose a distress detection method based on Vision Transformer using egocentric videos of engineers inspecting in subway tunnels. The proposed method detects distresses in subway tunnels by fine-tuned Vision Transformer trained by a large-scale image dataset. Furthermore, we train Vision Transformer by DINO, a self-supervised learning method, to generate an attention map that can be used as a reason for the distress detection result. As a result, the proposed method achieves highly accurate detection of distresses in subway tunnels and can provide a reason for the distress detection results. In the last part of this paper, we confirm the effectiveness of the proposed method by the experiment using actual egocentric videos of engineers inspecting in subway tunnels.
• Popularity-Aware Graph Social Recommendation for Fully Non-Interaction Users.
  Nozomu Onodera; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  Proceedings of the 4th ACM International Conference on Multimedia in Asia(MMAsia), 30, 5, ACM, 2022
  International conference proceedings
• Affective Embedding Framework with Semantic Representations from Tweets for Zero-Shot Visual Sentiment Prediction.
  Yingrui Ye; Yuya Moroto; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  Proceedings of the 4th ACM International Conference on Multimedia in Asia(MMAsia), 6, 7, ACM, 2022
  International conference proceedings
• Visual Sentiment Prediction Using Cross-Way Few-Shot Learning Based on Knowledge Distillation.
  Yingrui Ye; Yuya Moroto; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  2022 IEEE International Conference on Image Processing(ICIP), 3838, 3842, IEEE, 2022
  International conference proceedings
• Human-Centric Image Retrieval with Gaze-Based Image Captioning.
  Yuhu Feng; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  2022 IEEE International Conference on Image Processing(ICIP), 3828, 3832, IEEE, 2022
  International conference proceedings
• Few-Shot Personalized Saliency Prediction with Similarity of Gaze Tendency Using Object-Based Structural Information.
  Yuya Moroto; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  2022 IEEE International Conference on Image Processing(ICIP), 3823, 3827, IEEE, 2022
  International conference proceedings
• Gaussian Distributed Graph Constrained Multi-Modal Gaussian Process Latent Variable Model for Ordinal Labeled Data.
  Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  2022 IEEE International Conference on Image Processing(ICIP), 3798, 3802, IEEE, 2022
  International conference proceedings
• GCN-Based Multi-Modal Multi-Label Attribute Classification in Anime Illustration Using Domain-Specific Semantic Features.
  Ziwen Lan; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  2022 IEEE International Conference on Image Processing(ICIP), 2021, 2025, IEEE, 2022
  International conference proceedings
• Trend Prediction of Students' Mock Examination Results Using Matrix Completion.
  Yutaka Yamada; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  11th IEEE Global Conference on Consumer Electronics(GCCE), 891, 892, IEEE, 2022
  International conference proceedings
• Shoot Event Prediction from Soccer Videos by Considering Players' Spatio-Temporal Relations.
  Ryota Goka; Yuya Moroto; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  11th IEEE Global Conference on Consumer Electronics(GCCE), 406, 407, IEEE, 2022
  International conference proceedings
• Refinement of Gaze-based Image Caption for Image Retrieval.
  Yuhu Feng; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  11th IEEE Global Conference on Consumer Electronics(GCCE), 272, 273, IEEE, 2022
  International conference proceedings
• Brain Decoding of Multiple Subjects for Estimating Visual Information Based on a Probabilistic Generative Model.
  Takaaki Higashi; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  Sensors, 22, 16, 6148, 6148, 2022
  Scientific journal
• Assessment of Image Manipulation Using Natural Language Description: Quantification of Manipulation Direction.
  Yuto Watanabe; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  ICIP, 1046, 1050, IEEE, 2022
  International conference proceedings
• Content-based Image Retrieval Using Effective Synthesized Images from Different Camera Views via pixelNeRF.
  Yuki Era; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  GCCE, 404, 405, IEEE, 2022
  International conference proceedings
• Analysis of Relationships between Visual Cognitive Contents and Response of Each Brain Region via Visual Question Answering.
  Ryo Shichida; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  GCCE, 402, 403, IEEE, 2022
  International conference proceedings
• GCN-based Collaborative Filtering Considering Personality Bias.
  Hiroki Okamura; Keisuke Maeda; Ren Togo; Takahiro Ogawa 0001; Miki Haseyama
  GCCE, 278, 279, IEEE, 2022
  International conference proceedings
• Prediction of Amyloid-β Positivity Using QSM Images Based on Bootstrap Your Own Latent.
  Tsubasa Kunieda; Ren Togo; Noriko Nishioka; Yukie Shimizu; Shiro Watanabe; Kenji Hirata; Keisuke Maeda; Takahiro Ogawa 0001; Kohsuke Kudo; Miki Haseyama
  GCCE, 137, 138, IEEE, 2022
  International conference proceedings
• Cross-platform Recommendation Considering Common Users' Preferences Based on Preference Propagation GraphNet.
  Kazuki Yamamoto; Keisuke Maeda; Ren Togo; Takahiro Ogawa 0001; Miki Haseyama
  GCCE, 135, 136, IEEE, 2022
  International conference proceedings
• Trial Analysis of the Relationship between Taste and Biological Information Obtained While Eating Strawberries for Sensory Evaluation.
  Keisuke Maeda; Ren Togo; Takahiro Ogawa 0001; Shin-ichi Adachi; Fumiaki Yoshizawa; Miki Haseyama
  Sensors, 22, 23, 9496, 9496, 2022
  Scientific journal
• Distress Detection in Subway Tunnel Images via Data Augmentation Based on Selective Image Cropping and Patching.
  Keisuke Maeda; Saya Takada; Tomoki Haruyama; Ren Togo; Takahiro Ogawa 0001; Miki Haseyama
  Sensors, 22, 22, 8932, 8932, 2022
  Scientific journal
• Generative Adversarial Network Including Referring Image Segmentation For Text-Guided Image Manipulation.
  Yuto Watanabe; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  ICASSP, 4818, 4822, IEEE, 2022, [Peer-reviewed]
  International conference proceedings
• Human Emotion Recognition Using Multi-Modal Biological Signals Based On Time Lag-Considered Correlation Maximization.
  Yuya Moroto; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  ICASSP, 4683, 4687, IEEE, 2022, [Peer-reviewed]
  International conference proceedings
• Distributed Label Dequantized Gaussian Process Latent Variable Model for Multi-View Data Integration.
  Koshi Watanabe; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  ICASSP, 4643, 4647, IEEE, 2022, [Peer-reviewed]
  International conference proceedings
• Variational Bayesian Graph Convolutional Network for Robust Collaborative Filtering.
  Nozomu Onodera; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  ICASSP, 3908, 3912, IEEE, 2022, [Peer-reviewed]
  International conference proceedings
• Time-Lag Aware Latent Variable Model for Prediction of Important Scenes Using Baseball Videos and Tweets.
  Kaito Hirasawa; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  Sensors, 22, 7, 2465, 2465, 2022, [Peer-reviewed]
  English, Scientific journal
• Deterioration Level Estimation Based on Convolutional Neural Network Using Confidence-Aware Attention Mechanism for Infrastructure Inspection.
  Naoki Ogawa; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  Sensors, 22, 1, 382, 382, 2022, [Peer-reviewed]
  English, Scientific journal
• Refining Graph Representation for Cross-Domain Recommendation Based on Edge Pruning in Latent Space.
  Taisei Hirakawa; Keisuke Maeda; Takahiro Ogawa 0001; Satoshi Asamizu; Miki Haseyama
  IEEE Access, 10, 12503, 12509, 2022, [Peer-reviewed]
  English, Scientific journal
• Cross-domain recommendation based on multilayer graph analysis using subgraph representation
  Taisei Hirakawa; Keisuke Maeda; Takahiro Ogawa; Satoshi Asamizu; Miki Haseyama
  Proceedings of SPIE - The International Society for Optical Engineering, 11766, 2021, [Peer-reviewed]
  English, International conference proceedings
• Cross-Domain Recommendation Method Based On Multi-Layer Graph Analysis With Visual Information.
  Taisei Hirakawa; Keisuke Maeda; Takahiro Ogawa 0001; Satoshi Asamizu; Miki Haseyama
  ICIP, 2688, 2692, 2021, [Peer-reviewed]
  English, International conference proceedings
• Time-Lag Aware Multi-Modal Variational Autoencoder Using Baseball Videos And Tweets For Prediction Of Important Scenes.
  Kaito Hirasawa; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  ICIP, 2678, 2682, 2021, [Peer-reviewed]
  English, International conference proceedings
• Segmentation-Aware Text-Guided Image Manipulation.
  Tomoki Haruyama; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  ICIP, 2433, 2437, IEEE, 2021, [Peer-reviewed]
  International conference proceedings
• Few-Shot Personalized Saliency Prediction using Person Similarity based on Collaborative Multi-Output Gaussian Process Regression.
  Yuya Moroto; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  ICIP, 1469, 1473, 2021, [Peer-reviewed]
  English, International conference proceedings
• Interest Level Estimation via Multi-Modal Gaussian Process Latent Variable Factorization.
  Kyohei Kamikawa; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  ICIP, 1209, 1213, 2021, [Peer-reviewed]
  English, International conference proceedings
• Deep Metric Network Via Heterogeneous Semantics for Image Sentiment Analysis.
  Yun Liang 0014; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  ICIP, 1039, 1043, 2021
  English, International conference proceedings
• Correlation-Aware Attention Branch Network Using Multi-Modal Data For Deterioration Level Estimation Of Infrastructures.
  Naoki Ogawa; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  ICIP, 1014, 1018, 2021, [Peer-reviewed]
  English, International conference proceedings
• Degradation Level Estimation of Road Structures via Attention Branch Network with Text Data.
  Naoki Ogawa; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  IEEE International Conference on Consumer Electronics-Taiwan(ICCE-TW), 1, 2, IEEE, 2021, [Peer-reviewed]
  International conference proceedings
• Cross-Domain Semi-Supervised Deep Metric Learning for Image Sentiment Analysis.
  Yun Liang 0014; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP), 4150, 4154, 2021, [Peer-reviewed]
  English, International conference proceedings
• Feature Integration via Semi-Supervised Ordinally Multi-Modal Gaussian Process Latent Variable Model.
  Kyohei Kamikawa; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP), 4130, 4134, 2021, [Peer-reviewed]
  English, International conference proceedings
• Multi-Modal Label Dequantized Gaussian Process Latent Variable Model for Ordinal Label Estimation.
  Masanao Matsumoto; Keisuke Maeda; Naoki Saito 0006; Takahiro Ogawa 0001; Miki Haseyama
  IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP), 3985, 3989, 2021, [Peer-reviewed]
  English, International conference proceedings
• Classification of Expert-Novice Level Using Eye Tracking And Motion Data via Conditional Multimodal Variational Autoencoder.
  Yusuke Akamatsu; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP), 1360, 1364, 2021, [Peer-reviewed]
  English, International conference proceedings
• Estimation of Visual Features of Viewed Image From Individual and Shared Brain Information Based on FMRI Data Using Probabilistic Generative Model.
  Takaaki Higashi; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP), 1335, 1339, IEEE, 2021, [Peer-reviewed]
  International conference proceedings
• Defense Against Image Captioning Attacks via A Robust and Stable Recurrent Neural Network.
  Jiahuan Zhang; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  GCCE, 785, 786, IEEE, 2021, [Peer-reviewed]
  International conference proceedings
• Text-Guided Image Manipulation for Desired Region Using Referring Image Segmentation.
  Yuto Watanabe; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  GCCE, 661, 662, IEEE, 2021, [Peer-reviewed]
  International conference proceedings
• A Trial of Fine-grained Classification of Expert-novice Level Using Bio-signals While Inspecting Subway Tunnels.
  Kaito Hirasawa; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  GCCE, 204, 205, IEEE, 2021, [Peer-reviewed]
  International conference proceedings
• Movie Rating Estimation Based on Weakly Supervised Multi-modal Latent Variable Model.
  Koshi Watanabe; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  GCCE, 195, 196, IEEE, 2021, [Peer-reviewed]
  International conference proceedings
• Visual Sentiment Prediction Using Few-shot Learning via Distribution Relations of Visual Features.
  Yingrui Ye; Yuya Moroto; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  GCCE, 191, 192, IEEE, 2021, [Peer-reviewed]
  International conference proceedings
• Analysis of Social Trends Related to COVID-19 Pandemic Utilizing Social Media Data.
  Taisei Hirakawa; Keisuke Maeda; Takahiro Ogawa 0001; Satoshi Asamizu; Miki Haseyama
  GCCE, 43, 44, IEEE, 2021, [Peer-reviewed]
  International conference proceedings
• Graph Analysis-based Recommendation via Entity Embeddings Using Wikipedia.
  Nozomu Onodera; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  GCCE, 5, 6, IEEE, 2021, [Peer-reviewed]
  International conference proceedings
• Deterioration level estimation via neural network maximizing category-based ordinally supervised multi-view canonical correlation.
  Keisuke Maeda; Sho Takahashi; Takahiro Ogawa 0001; Miki Haseyama
  Multimedia Tools and Applications, 80, 15, 23091, 23112, 2021, [Peer-reviewed], [Lead author]
  English, Scientific journal
• Reliable Estimation of Deterioration Levels via Late Fusion Using Multi-View Distress Images for Practical Inspection.
  Keisuke Maeda; Naoki Ogawa; Takahiro Ogawa 0001; Miki Haseyama
  Journal of Imaging, 7, 12, 273, 273, 2021, [Peer-reviewed], [Lead author]
  English, Scientific journal
• Feature Integration Through Semi-Supervised Multimodal Gaussian Process Latent Variable Model With Pseudo-Labels for Interest Level Estimation.
  Kyohei Kamikawa; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  IEEE Access, 9, 163843, 163850, 2021, [Peer-reviewed]
  English, Scientific journal
• Detection of Important Scenes in Baseball Videos via Bidirectional Time Lag Aware Deep Multiset Canonical Correlation Analysis.
  Kaito Hirasawa; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  IEEE Access, 9, 84971, 84981, 2021, [Peer-reviewed]
  English, Scientific journal
• Human Emotion Estimation Using Multi-Modal Variational AutoEncoder with Time Changes.
  Yuya Moroto; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  3rd IEEE Global Conference on Life Sciences and Technologies(LifeTech), 67, 68, 2021, [Peer-reviewed]
  English, International conference proceedings
• Detection of Important Scenes in Baseball Videos via a Time-Lag-Aware Multimodal Variational Autoencoder.
  Kaito Hirasawa; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  Sensors, 21, 6, 2045, 2045, 2021, [Peer-reviewed]
  English, Scientific journal
• Distress Image Retrieval for Infrastructure Maintenance via Self-Trained Deep Metric Learning Using Experts' Knowledge.
  Naoki Ogawa; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  IEEE Access, 9, 65234, 65245, 2021, [Peer-reviewed]
  English, Scientific journal
• Rubber Material Property Prediction Using Electron Microscope Images of Internal Structures Taken under Multiple Conditions.
  Ren Togo; Naoki Saito; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  Sensors, 21, 6, 2088, 2088, 2021, [Peer-reviewed]
  English, Scientific journal
• Supervised Fractional-Order Embedding Multiview Canonical Correlation Analysis via Ordinal Label Dequantization for Image Interest Estimation
  Masanao Matsumoto; Naoki Saito; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  IEEE Access, 9, 21810, 21822, 2021, [Peer-reviewed]
  English, Scientific journal
• Important Scene Detection Based on Anomaly Detection using Long Short-Term Memory for Baseball Highlight Generation
  Kaito Hirasawa; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020, 1, 2, 28 Sep. 2020, [Peer-reviewed]
  English, International conference proceedings
• Human-centered image classification via a neural network considering visual and biological features.
  Kazaha Horii; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  Multimedia Tools Appl., 79, 7-8, 4395, 4415, 2020, [Peer-reviewed]
  English, Scientific journal
• Image retrieval based on supervised local regression and global alignment with relevance feedback for insect identification
  Keisuke Maeda; Susumu Genma; Takahiro Ogawa; Miki Haseyama
  ITE Transactions on Media Technology and Applications, 8, 3, 140, 150, 2020, [Peer-reviewed], [Lead author]
  English, Scientific journal
• Interpretable convolutional neural network including attribute estimation for image classification
  Kazaha Horii; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  ITE Transactions on Media Technology and Applications, 8, 2, 111, 124, 2020, [Peer-reviewed]
  English, Scientific journal
• Estimation of Person-Specific Visual Attention via Selection of Similar Persons
  Yuya Moroto; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  2020 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS - TAIWAN (ICCE-TAIWAN), 1, 2, 2020, [Peer-reviewed]
  English, International conference proceedings
• Quantitative Analysis of Engineer's Skill Using Wearable Sensor Data while Inspecting Highway Bridge.
  Genki Suzuki; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  2nd IEEE Global Conference on Life Sciences and Technologies(LifeTech), 111, 112, IEEE, 2020, [Peer-reviewed]
  International conference proceedings
• Distress Level Classification of Road Infrastructures via CNN Generating Attention Map.
  Naoki Ogawa; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  2nd IEEE Global Conference on Life Sciences and Technologies(LifeTech), 97, 98, IEEE, 2020, [Peer-reviewed]
  International conference proceedings
• Mvgan Maximizing Time-Lag Aware Canonical Correlation for Baseball Highlight Generation.
  Kaito Hirasawa; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  2020 IEEE International Conference on Multimedia & Expo Workshops, 1, 6, 2020, [Peer-reviewed]
  English, International conference proceedings
• Important Scene Detection Of Baseball Videos Via Time-Lag Aware Deep Multiset Canonical Correlation Maximization.
  Kaito Hirasawa; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  IEEE International Conference on Image Processing(ICIP), 1236, 1240, 2020, [Peer-reviewed]
  English, International conference proceedings
• Feature Integration Via Geometrical Supervised Multi-View Multi-Label Canonical Correlation For Incomplete Label Assignment.
  Keisuke Maeda; Sho Takahashi; Takahiro Ogawa; Miki Haseyama
  IEEE International Conference on Image Processing(ICIP), 46, 50, 2020, [Peer-reviewed], [Lead author]
  English, International conference proceedings
• Interest Level Estimation Based on Feature Integration Considering Distribution of Partially Paired User's Behavior, Videos and Posters.
  Kyohei Kamikawa; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  9th IEEE Global Conference on Consumer Electronics(GCCE), 944, 945, IEEE, 2020, [Peer-reviewed]
  International conference proceedings
• Estimation of Images Matched with Audio-Induced Brain Activity via Modified DGCCA.
  Yun Liang 0014; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  9th IEEE Global Conference on Consumer Electronics(GCCE), 940, 941, IEEE, 2020, [Peer-reviewed]
  International conference proceedings
• Estimation of User-Specific Visual Attention Considering Individual Tendency toward Gazed Objects.
  Yuya Moroto; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  9th IEEE Global Conference on Consumer Electronics(GCCE), 745, 746, IEEE, 2020, [Peer-reviewed]
  International conference proceedings
• Estimation of Viewed Images Using Individual and Shared Brain Responses.
  Takaaki Higashi; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  9th IEEE Global Conference on Consumer Electronics(GCCE), 716, 717, IEEE, 2020, [Peer-reviewed]
  International conference proceedings
• Cross-domain Recommendation via Multi-layer Graph Analysis Using User-item Embedding.
  Taisei Hirakawa; Keisuke Maeda; Takahiro Ogawa; Satoshi Asamizu; Miki Haseyama
  9th IEEE Global Conference on Consumer Electronics(GCCE), 714, 715, IEEE, 2020, [Peer-reviewed]
  International conference proceedings
• Important Scene Prediction of Baseball Videos Using Twitter and Video Analysis Based on LSTM.
  Kaito Hirasawa; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  9th IEEE Global Conference on Consumer Electronics(GCCE), 636, 637, IEEE, 2020, [Peer-reviewed]
  International conference proceedings
• Few-Shot Personalized Saliency Prediction Based on Adaptive Image Selection Considering Object and Visual Attention.
  Yuya Moroto; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  Sensors, 20, 8, 2170, 2170, 2020, [Peer-reviewed]
  English, Scientific journal
• Tensor-Based Emotional Category Classification via Visual Attention-Based Heterogeneous CNN Feature Fusion.
  Yuya Moroto; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  Sensors, 20, 7, 2146, 2146, 2020, [Peer-reviewed]
  English, Scientific journal
• Multi-Task Convolutional Neural Network Leading to High Performance and Interpretability via Attribute Estimation.
  Keisuke Maeda; Kazaha Horii; Takahiro Ogawa; Miki Haseyama
  IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 103-A, 12, 1609, 1612, 2020, [Peer-reviewed], [Lead author]
  English, Scientific journal
• Inpainting via Sparse Representation Based on a Phaseless Quality Metric.
  Takahiro Ogawa; Keisuke Maeda; Miki Haseyama
  IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 103-A, 12, 1541, 1551, 2020, [Peer-reviewed]
  English, Scientific journal
• Human-Centric Emotion Estimation Based on Correlation Maximization Considering Changes With Time in Visual Attention and Brain Activity.
  Yuya Moroto; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  IEEE Access, 8, 203358, 203368, 2020, [Peer-reviewed]
  English, Scientific journal
• Estimation of Interest Levels From Behavior Features via Tensor Completion Including Adaptive Similar User Selection.
  Keisuke Maeda; Tetsuya Kushima; Sho Takahashi; Takahiro Ogawa; Miki Haseyama
  IEEE Access, 8, 126109, 126118, 2020, [Peer-reviewed], [Lead author]
  English, Scientific journal
• Supervised Fractional-Order Embedding Geometrical Multi-View CCA (SFGMCCA) for Multiple Feature Integration.
  Keisuke Maeda; Yoshiki Ito; Takahiro Ogawa; Miki Haseyama
  IEEE Access, 8, 114340, 114353, 2020, [Peer-reviewed], [Lead author]
  English, Scientific journal
• Region-based Distress Classification of Road Infrastructures via CNN Without Region Annotation
  N. Ogawa; K. Maeda; T. Ogawa; M. Haseyama
  IEEE Global Conference on Consumer Electronics (GCCE), 764, 765, IEEE, Oct. 2019, [Peer-reviewed]
  English, International conference proceedings
• Semantic Shot Classification in Baseball Videos Based on Similarities of Visual Features
  K. Hirasawa; K. Maeda; T. Ogawa; M. Haseyama
  IEEE Global Conference on Consumer Electronics (GCCE), 663, 664, IEEE, Oct. 2019, [Peer-reviewed]
  English, International conference proceedings
• User-Specific Visual Attention Estimation Based on Visual Similarity and Spatial Information in Images
  Y. Moroto; K. Maeda; T. Ogawa; M. Haseyama
  IEEE International Conference on Consumer Electronics – Taiwan (ICCE-TW), 479, 480, May 2019, [Peer-reviewed]
  English, International conference proceedings
• Estimation of visual attention via canonical correlation between visual and gaze-based features
  Yuya Moroto; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  2019 IEEE 1st Global Conference on Life Sciences and Technologies, LifeTech 2019, 229, 230, IEEE, Mar. 2019, [Peer-reviewed]
  English, International conference proceedings
• Estimation of Emotion Labels via Tensor-Based Spatiotemporal Visual Attention Analysis.
  Yuya Moroto; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  IEEE International Conference on Image Processing (IEEE ICIP), 4105, 4109, 2019, [Peer-reviewed]
  English, International conference proceedings
• Neural Network Maximizing Ordinally Supervised Multi-View Canonical Correlation for Deterioration Level Estimation.
  Keisuke Maeda; Sho Takahashi; Takahiro Ogawa; Miki Haseyama
  IEEE International Conference on Image Processing (IEEE ICIP), 919, 923, 2019, [Peer-reviewed]
  English, International conference proceedings
• Estimation of User-Specific Visual Attention Based on Gaze Information of Similar Users.
  Yuya Moroto; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  IEEE Global Conference on Consumer Electronics (GCCE), 477, 478, IEEE, 2019, [Peer-reviewed]
  English, International conference proceedings
• Multi-feature Fusion Based on Supervised Multi-view Multi-label Canonical Correlation Projection.
  Keisuke Maeda; Sho Takahashi; Takahiro Ogawa; Miki Haseyama
  2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 3936, 3940, 2019, [Peer-reviewed], [Lead author]
  English, International conference proceedings
• Convolutional sparse coding-based deep random vector functional link network for distress classification of road structures.
  Keisuke Maeda; Sho Takahashi; Takahiro Ogawa; Miki Haseyama
  Comput. Aided Civ. Infrastructure Eng., 34, 8, 654, 676, 2019, [Peer-reviewed], [Lead author]
  English, Scientific journal
• A Human-Centered Neural Network Model with Discriminative Locality Preserving Canonical Correlation Analysis for Image Classification.
  Kazaha Horii; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  2018 IEEE International Conference on Image Processing, ICIP 2018, Athens, Greece, October 7-10, 2018, 2366, 2370, Oct. 2018, [Peer-reviewed]
  English, International conference proceedings
• Distress classification of class-imbalanced inspection data via correlation-maximizing weighted extreme learning machine
  Keisuke Maeda; Sho Takahashi; Takahiro Ogawa; Miki Haseyama
  Advanced Engineering Informatics, 37, 79, 87, 01 Aug. 2018, [Peer-reviewed], [Lead author]
  English, Scientific journal
• Estimation of Deterioration Levels of Transmission Towers via Deep Learning Maximizing Canonical Correlation Between Heterogeneous Features
  Maeda Keisuke; Takahashi Sho; Ogawa Takahiro; Haseyama Miki
  IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 12, 4, 633, 644, Aug. 2018, [Peer-reviewed], [Lead author]
  English, Scientific journal
• Automatic estimation of deterioration level on transmission towers via deep extreme learning machine based on local receptive field
  Keisuke Maeda; Sho Takahashi; Takahiro Ogawa; Miki Haseyama
  Proceedings - International Conference on Image Processing, ICIP, 2017-, 2379, 2383, 20 Feb. 2018, [Peer-reviewed]
  English, International conference proceedings
• Automatic martian dust storm detection via decision level fusion basedondeep extreme learning machine
  Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  Proceedings - International Conference on Image Processing, ICIP, 2017-, 435, 439, 20 Feb. 2018, [Peer-reviewed]
  English, International conference proceedings
• User-centric Visual Attention Estimation Based on Relationship between Image and Eye Gaze Data.
  Yuya Moroto; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  IEEE 7th Global Conference on Consumer Electronics, GCCE 2018, Nara, Japan, October 9-12, 2018, 73, 74, 2018, [Peer-reviewed]
  English, International conference proceedings
• Favorite Video Classification Based on Multimodal Bidirectional LSTM
  T. Ogawa; Y. Sasaka; K. Maeda; M. Haseyama
  IEEE ACCESS, 6, 61401, 61409, 2018, [Peer-reviewed]
  English, Scientific journal
• Image classification for trend prediction based on integration of fNIRS and visual features
  Kazaha Horii; Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017, 2017-, 1, 2, 19 Dec. 2017, [Peer-reviewed]
  English, International conference proceedings
• Distress Classification of Road Structures via Adaptive Bayesian Network Model Selection
  K. Maeda; S. Takahashi; T. Ogawa; M. Haseyama
  JOURNAL OF COMPUTING IN CIVIL ENGINEERING, 31, 5, 1, 13, Sep. 2017, [Peer-reviewed], [Lead author]
  English, Scientific journal
• Deterioration Level Estimation on Transmission Towers via Extreme Learning Machine based on Combination Use of Local Receptive Field and Principal Component Analysis
  K. Maeda; S. Takahashi; T. Ogawa; M. Haseyama
  International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), 457, 458, Jul. 2017, [Peer-reviewed]
  English, International conference proceedings
• Distress Classification of Class Imbalanced Data for Maintenance Inspection of Road Structures in Express Way
  K. Maeda; S. Takahashi; T. Ogawa; M. Haseyama
  International Conference on Civil and Building Engineering Informatics in conjunction with Conference on Computer Applications in Civil and Hydraulic Engineering (ICCBEI & CCACHE), 182, 185, Apr. 2017, [Peer-reviewed]
  English, International conference proceedings
• Distress Classification of Road Structures via Multiple Classifier-based Bayesian Network
  K. Maeda; S. Takahashi; T. Ogawa; M. Haseyama
  International Workshop on Advanced Image Technology (IWAIT), 1, 4, 2016, [Peer-reviewed]
  English, International conference proceedings
• Distress Classification of Road Structures via Decision Level Fusion
  Keisuke Maeda; Sho Takahashi; Takahiro Ogawa; Miki Haseyama
  2016 IEEE INTERNATIONAL CONFERENCE ON DIGITAL SIGNAL PROCESSING (DSP), 589, 593, 2016, [Peer-reviewed]
  English, International conference proceedings
• Automatic Martian Dust Storm Detection from Multiple Wavelength Data Based on Decision Level Fusion
  Maeda Keisuke; Ogawa Takahiro; Haseyama Miki
  IMT, 10, 3, 473, 477, Information and Media Technologies Editorial Board, 2015, [Peer-reviewed], [Lead author]
  English, Scientific journal, This paper presents automatic Martian dust storm detection from multiple wavelength data based on decision level fusion. In our proposed method, visual features are first extracted from multiple wavelength data, and optimal features are selected for Martian dust storm detection based on the minimal-Redundancy-Maximal-Relevance algorithm. Second, the selected visual features are used to train the Support Vector Machine classifiers that are constructed on each data. Furthermore, as a main contribution of this paper, the proposed method integrates the multiple detection results obtained from heterogeneous data based on decision level fusion, while considering each classifiers detection performance to obtain accurate final detection results. Consequently, the proposed method realizes successful Martian dust storm detection.
• AUTOMATIC DETECTION OF MARTIAN DUST STORMS FROM HETEROGENEOUS DATA BASED ON DECISION LEVEL FUSION
  Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  2015 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2246, 2250, 2015, [Peer-reviewed]
  English, International conference proceedings
• Automatic martian dust storm detection from multiple wavelength data based on decision level fusion
  Keisuke Maeda; Takahiro Ogawa; Miki Haseyama
  IPSJ Transactions on Computer Vision and Applications, 7, 79, 83, Information Processing Society of Japan, 2015, [Peer-reviewed]
  English, Scientific journal
• Bayesian network-based distress estimation using image features in road structure assessment
  Keisuke Maeda; Sho Takahashi; Takahiro Ogawa; Miki Haseyama
  2014 IEEE 3rd Global Conference on Consumer Electronics, GCCE 2014, 169, 170, 03 Feb. 2014, [Peer-reviewed]
  English, International conference proceedings

• Text region detection and LMM-based digit recognition in scanned drawings
  福井千菜美; WANG Chang; LAN Ziwen; LI Guang; 田村彰浩; 花田剛志; 前田圭介; 高橋翔; 小川貴弘; 長谷山美紀, AI・データサイエンス論文集(Web), 6, 2, 2025
• Dynamic analysis of factory and environmental factors affecting properties of rubber materials based on causal inference
  ZHANG Huaying; 藤後廉; 前田圭介; 小川貴弘; 長谷山美紀, 人工知能学会全国大会論文集(Web), 39th, 2025
• A Note on Improving Accuracy in Composed Image Retrieval through Training Data Generation-Introduction of a Counterfactual Image Generation Model with Text Refinement-
  上杉健大; 斉藤直輝; 前田圭介; 小川貴弘; 長谷山美紀, 人工知能学会全国大会論文集(Web), 39th, 2025
• A note on motion recognition for tire inspection based on the cooperative use of object tracking models and video-LLMs
  上川恭平; 藤後廉; 前田圭介; 小川貴弘; 長谷山美紀, 人工知能学会全国大会論文集(Web), 39th, 2025
• A Note on Performance Improvement of Visual Emotion Classification via Multimodal LLM Introducing Text Prompt Optimization
  高橋諒; 斉藤直輝; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告(Web), 49, 4(MMS2025 1-40/ME2025 1-40/AIT2025 1-40/SIP2025 1-40), 2025
• Withered Tree Detection Technology by Semantic Segmentation and Depth Estimation for Efficient Daily Inspection on Highways
  斉藤直輝; 山本一輝; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告(Web), 49, 4(MMS2025 1-40/ME2025 1-40/AIT2025 1-40/SIP2025 1-40), 2025
• A Note on Sensitivity Evaluation of Novel View Synthesis Metrics in 3D Scenes with Limited Conditions
  WANG Haoyang; 藤後廉; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告(Web), 49, 4(MMS2025 1-40/ME2025 1-40/AIT2025 1-40/SIP2025 1-40), 2025
• Damage Classification Using Road Attachment Images Based on Vision Transformer and Vision Language Model
  渡部航史; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告(Web), 49, 4(MMS2025 1-40/ME2025 1-40/AIT2025 1-40/SIP2025 1-40), 2025
• Effectiveness Verification of Introducing Model Merging in Federated Learning-Investigation from Image Classification Tasks Targeting Multiple Domains-
  久保田健太; 藤後廉; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告(Web), 49, 4(MMS2025 1-40/ME2025 1-40/AIT2025 1-40/SIP2025 1-40), 2025
• Event Location Prediction from Urgent Calls based on Fine-tuning of Speech Recognition Models for Geographic Name Recognition
  吉田将規; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告(Web), 49, 4(MMS2025 1-40/ME2025 1-40/AIT2025 1-40/SIP2025 1-40), 2025
• A Note on Supporting Interior Coordination Using Image Generation and Complementary Recommendation Techniques
  櫻井慶悟; 岡村洋希; 藤後廉; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告(Web), 49, 4(MMS2025 1-40/ME2025 1-40/AIT2025 1-40/SIP2025 1-40), 2025
• Technology of Finding Generation Using Vision Language Model for Efficient Bridge Inspection on Highway
  清野竜生; 斉藤直輝; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告(Web), 49, 4(MMS2025 1-40/ME2025 1-40/AIT2025 1-40/SIP2025 1-40), 2025
• A Note on Personalized Anomaly Detection Based on Vision Language Model Using Image Prompt
  松田遥; 藤後廉; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告(Web), 49, 4(MMS2025 1-40/ME2025 1-40/AIT2025 1-40/SIP2025 1-40), 2025
• A Note on the Effectiveness of Brain Activity Information Against Adversarial Attacks-Utilization of Image Reconstruction Method from Brain Signals Using Generative Models-
  中島佑; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告(Web), 49, 4(MMS2025 1-40/ME2025 1-40/AIT2025 1-40/SIP2025 1-40), 2025
• A Note on Image-to-music Generation via Musical Caption Based on In-context Learning
  LIU Shilin; 上川恭平; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告(Web), 49, 4(MMS2025 1-40/ME2025 1-40/AIT2025 1-40/SIP2025 1-40), 2025
• A Note on Consideration of Spatial Integrity in Continuous 3D Scene Generation Method
  江良勇輝; 藤後廉; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告(Web), 49, 4(MMS2025 1-40/ME2025 1-40/AIT2025 1-40/SIP2025 1-40), 2025
• A Note on Interpretability of Visual Language Model by Few-shot Learning Based on the Linear Representation Hypothesis
  岡村洋希; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告(Web), 49, 4(MMS2025 1-40/ME2025 1-40/AIT2025 1-40/SIP2025 1-40), 2025
• Advanced Finding Generation AI Based on In-context Learning for Inspection Report Creation
  佐藤雅也; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告(Web), 49, 4(MMS2025 1-40/ME2025 1-40/AIT2025 1-40/SIP2025 1-40), 2025
• Robust Adversarial Defense Based on Non-Transferability of Attack Across Foundation Models.
  Koshiro Toishi; Keisuke Maeda; Ren Togo; Takahiro Ogawa 0001; Miki Haseyama, ICASSP, 2024, 1, 5, 2025
• AI research for DX realization in the field of infrastructure maintenance
  前田圭介; 小川貴弘; 長谷山美紀, 画像ラボ, 35, 7, 2024
• A Note on Similar Case Retrieval via Deep Metric Learning Using Sensor Data Obtained from Semiconductor Manufacturing Equipment
  斉藤直輝; 藤後廉; 前田圭介; 小林累輝; 中村隆央; 岡谷基弘; 数井誠人; 松沢貴仁; 小川貴弘; 長谷山美紀, 人工知能学会全国大会論文集(Web), 38th, 2024
• Investigation on estimation of factory and environmental factors affecting properties of rubber materials
  柳凜太郎; 藤後廉; 前田圭介; 小川貴弘; 長谷山美紀, 人工知能学会全国大会論文集(Web), 38th, 2024
• Prediction of Event Locations from Urgent Call Using Speech Recognition and Generative AI
  吉田将規; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告(Web), 48, 6(MMS2024 1-30/ME2024 17-46/AIT2024 1-30), 2024
• Research for Next-generation Infrastructure Maintenance and Activity of Collaboration Agreement with Hokkaido Regional Development Bureau
  前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告(Web), 48, 6(MMS2024 1-30/ME2024 17-46/AIT2024 1-30), 2024
• A Note on Domain Adaptation by Setting Features of Interest in Visual Language Models
  岡村洋希; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告(Web), 48, 6(MMS2024 1-30/ME2024 17-46/AIT2024 1-30), 2024
• Damage Detection Using Drone Videos of Road Attachments Based on Vision Transformer
  渡部航史; 小川直輝; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告(Web), 48, 6(MMS2024 1-30/ME2024 17-46/AIT2024 1-30), 2024
• Technology of Findings Generation Based on Generative AI for Efficient Bridge Inspection
  渡邉優宇人; 小川直輝; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告(Web), 48, 6(MMS2024 1-30/ME2024 17-46/AIT2024 1-30), 2024
• A Note on Action Estimation with Event Stream Data of Soccer Player Based on Bidirectional Transformer
  五箇亮太; 諸戸祐哉; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告(Web), 48, 6(MMS2024 1-30/ME2024 17-46/AIT2024 1-30), 2024
• A Note on Text to Image Generation Based on Stable Diffusion Using Brain Activity Data While Gazing on Image-Introduction of Controllable Mechanism with Brain Activity Data in Latent Space-
  七田亮; 藤後廉; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告(Web), 48, 6(MMS2024 1-30/ME2024 17-46/AIT2024 1-30), 2024
• A Note on Improving Robustness of CLIP by Adversarial Training Enhanced with Brain Activity
  中島佑; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2024, 2024
• A Note on Model Generalization Based on Generated Images Using Data Selection Considering Class Information
  早川楓; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2024, 2024
• A Note on Improvement of Non-hierarchical Clients Clustering Utilizing Model Learning Trajectories on Personalized Federated Learning
  久保田健太; 藤後廉; 前田圭介; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2024, 2024
• A note on emotion classification of images by multimodal LLM considering the similarity of individual emotion elicitation
  高橋諒; 斉藤直輝; 前田圭介; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2024, 2024
• A Note on Data Augmentation in Composed Image Retrieval Using Counterfactual Image Generation Model
  上杉健大; 斉藤直輝; 前田圭介; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2024, 2024
• Analysis of self-consuming training loops for music generation
  LIU Shilin; 上川恭平; 前田圭介; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2024, 2024
• An Evaluation Metric for Single Image-to-3D Models Based on a Class Confidence Score of Object Detection Models.
  Yuiko Uchida; Ren Togo; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama, GCCE, 2024, 1163, 1164, 2024
• Accident risk estimation with depth information in construction site videos
  GOKA Ryota; MAEDA Keisuke; TOGO Ren; OGAWA Takahiro; HASEYAMA Miki, Proceedings of the Annual Conference of JSAI, JSAI2024, 2C6GS701, 2C6GS701, 2024
  In the construction industry, reducing accident risk and improving safety is one of the high-priority tasks. Recently, several methods have been proposed to estimate the contact accident risk with heavy machinery on construction sites for enhancing safety. Conventional studies based on deep learning estimate the risk by using relations within the image space of detected workers and machinery captured in construction site videos. However, these approaches focus on the distance between detected objects in the image, leading to the problem that accident risk is overestimated even when there is distance between objects in the real world. In this study, to consider 3D spatial information in videos, we propose a method for estimating the accident risk with visual features regarding depth information. Experimental results show that the proposed method performs better than existing methods in estimating the contact accident risk., The Japanese Society for Artificial Intelligence, Japanese
• サッカー映像における時空間的関係を考慮したシュート予測の高精度化に関する検討 : 競技者のチーム情報に基づく完全二部グラフの導入—A Note on Accurate Shoot Prediction Considering Players' Spatio-temporal Relations in Soccer Videos : Introduction of Complete Bipartite Graph Based on Players' Team Information—マルチメディアストレージ メディア工学 映像表現&コンピュータグラフィックス
  五箇 亮太; 諸戸 祐哉; 前田 圭介; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 47, 6, 23, 28, Feb. 2023
  東京 : 映像情報メディア学会, Japanese
• クラス情報を導入したグラフ表現による教師有り潜在変数モデルの高精度化に関する検討—A Note on Improvement of Supervised Latent Variable Model with Graph-Encoded Class Information—マルチメディアストレージ メディア工学 映像表現&コンピュータグラフィックス
  渡部 航史; 前田 圭介; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 47, 6, 29, 33, Feb. 2023
  東京 : 映像情報メディア学会, Japanese
• 属性情報の階層関係を考慮したアニメイラストのマルチラベル分類に関する検討—A Note on Multi-label Image Classification in Animation Illustration Considering Hierarchical Relationships of Attributes—マルチメディアストレージ メディア工学 映像表現&コンピュータグラフィックス
  蘭 子文; 前田 圭介; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 47, 6, 5, 9, Feb. 2023
  東京 : 映像情報メディア学会, Japanese
• ユーザの視線情報を考慮したコンテンツベースの画像再検索に関する検討—A note on gaze-dependent image re-ranking for content-based image retrieval—マルチメディアストレージ メディア工学 映像表現&コンピュータグラフィックス
  馮 鈺虎; 前田 圭介; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 47, 6, 89, 93, Feb. 2023
  東京 : 映像情報メディア学会, Japanese
• A Note on Improving Noisy Labels Learning via Label Correction Utilizing Pre-trained Models
  柏木將希; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2023, 2023
• A Note on Caption Unification for Multi-view Lifelogging Images Using In-context Learning
  佐藤雅也; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2023, 2023
• A Note on the Personalization for Multiple Objects in Image Generation Using a Diffusion Model
  松田遥; 藤後廉; 前田圭介; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2023, 2023
• A Note on the Estimation of Language Information from fMRI Using a Multimodal Large-Scale Language Model-Estimation of Language Information from Temporal Auditory Stimuli Based on In-context Learning-
  藤後太郎; 藤後廉; 前田圭介; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2023, 2023
• A Study on Evaluating the Role of Feature Extraction in Enhancing the Accuracy of Visual Counterfactual Machine Learning Models
  LI Xiang; 藤後廉; 前田圭介; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2023, 2023
• Canonical Correlation Analysis Introducing Label Dequantization for Visual Emotion Recognition
  斉藤直輝; 前田圭介; 小川貴弘; 浅水仁; 長谷山美紀, 電子情報通信学会論文誌 D(Web), J106-D, 5, 2023
• A Note on Multi-label Image Classification in Animation Illustration Considering Hierarchical Relationships of Attributes
  LAM Ziwen; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告, 47, 6(MMS2023 1-34/ME2023 21-54/AIT2023 1-34), 2023
• A Note on Improvement of Supervised Latent Variable Model with Graph-Encoded Class Information
  渡部航史; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告, 47, 6(MMS2023 1-34/ME2023 21-54/AIT2023 1-34), 2023
• A note on gaze-dependent image re-ranking for content-based image retrieval
  FENG Yuhu; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告, 47, 6(MMS2023 1-34/ME2023 21-54/AIT2023 1-34), 2023
• A Note on Accurate Shoot Prediction Considering Players’ Spatio-temporal Relations in Soccer Videos-Introduction of Complete Bipartite Graph Based on Players’ Team Information-
  五箇亮太; 諸戸祐哉; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告, 47, 6(MMS2023 1-34/ME2023 21-54/AIT2023 1-34), 2023
• A Note on Realizing Adversarial Defense Based on Regularization of Multi-stage Squeeze-and-Excitation Features: マルチメディアストレージ メディア工学 映像表現&コンピュータグラフィックス
  46, 6, 87, 90, Feb. 2022
  English
• 知識蒸留を用いたFew-shot Learningに基づく画像の感情ラベル推定に関する検討—A Note on Visual Sentiment Prediction Based on Few-shot Learning Using Knowledge Distillation—マルチメディアストレージ メディア工学 映像表現&コンピュータグラフィックス
  叶 穎睿; 諸戸 祐哉; 前田 圭介; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 46, 6, 171, 175, Feb. 2022
  東京 : 映像情報メディア学会, Japanese
• Captioning特徴を利用したグラフ畳み込みネットワークに基づくアニメイラストのマルチラベル画像分類に関する検討—A Note on Multi-label Image Recognition in Anime Illustration Based on Graph Convolutional Networks Using Captioning Features—マルチメディアストレージ メディア工学 映像表現&コンピュータグラフィックス
  蘭 子文; 前田 圭介; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 46, 6, 161, 165, Feb. 2022
  東京 : 映像情報メディア学会, Japanese
• ゴム材料開発のためのGenerative Adversarial Networkに基づく配合量および物性からの電子顕微鏡画像の生成に関する一検討—A Note on Electron Microscope Image Generation from Mix Proportion and Material Property via Generative Adversarial Network for Rubber Materials—マルチメディアストレージ メディア工学 映像表現&コンピュータグラフィックス
  柳 凛太郎; 藤後 廉; 前田 圭介; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 46, 6, 187, 191, Feb. 2022
  東京 : 映像情報メディア学会, Japanese
• 地下鉄トンネル点検時の技術者から取得される生体信号と技術者の点検行動の関連性分析—Relevance Analysis between Bio-signals of Engineers Inspecting Subway Tunnels and Their Inspection Behaviors—マルチメディアストレージ メディア工学 映像表現&コンピュータグラフィックス
  平澤 魁人; 前田 圭介; 藤後 廉; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 46, 6, 365, 370, Feb. 2022
  東京 : 映像情報メディア学会, Japanese
• 高速道路の遮音壁画像を用いた物体検出手法による変状分類の高精度化に関する検討—A note on improvement of distress classification using noise barrier images on highway via object detection method—マルチメディアストレージ メディア工学 映像表現&コンピュータグラフィックス
  梁 鋆; 前田 圭介; 藤後 廉; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 46, 6, 359, 363, Feb. 2022
  東京 : 映像情報メディア学会, Japanese
• 道路構造物の維持管理効率化に向けた変状画像分類の高精度化に関する検討 : テキストデータに基づく類似事例の含有率の導入—A Note on Improvement of Accuracy in Classification of Destress Images for Efficient Inspection of Road Structures : Introduction of Ratio of Similar Cases Based on Text Data—マルチメディアストレージ メディア工学 映像表現&コンピュータグラフィックス
  平川 泰成; 小川 直輝; 前田 圭介; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 46, 6, 43, 48, Feb. 2022
  東京 : 映像情報メディア学会, Japanese
• 地下鉄トンネルの維持管理支援のためのマルチスケール解析を導入した深層学習に基づく変状検出に関する検討—A Note on Distress Detection Based on Deep Learning with Hierarchical Multi-Scale Attention Mechanism for Supporting Maintenance of Subway Tunnels—マルチメディアストレージ メディア工学 映像表現&コンピュータグラフィックス
  高田 紗弥; 前田 圭介; 藤後 廉; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 46, 6, 377, 381, Feb. 2022
  東京 : 映像情報メディア学会, Japanese
• ユーザの動作情報を用いたコンテンツの関心度推定に関する検討 : 複数ユーザを導入した特徴統合の有効性検証—A Note on Interest level Estimation Using Users' behavior Information : Validating the Effectiveness of Feature Integration with Multiple Users—マルチメディアストレージ メディア工学 映像表現&コンピュータグラフィックス
  上川 恭平; 前田 圭介; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 46, 6, 103, 107, Feb. 2022
  東京 : 映像情報メディア学会, Japanese
• 画像注視時の脳活動信号を用いた圧縮再構成ネットワークに基づく視覚認知内容の推定に関する検討—A Note on Perceived Visual Content Estimation Based on Compressed Reconstruction Network Using Brain Signals While Gazing on Images—マルチメディアストレージ メディア工学 映像表現&コンピュータグラフィックス
  東 孝明; 前田 圭介; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 46, 6, 349, 353, Feb. 2022
  東京 : 映像情報メディア学会, Japanese
• Attention mapに対する確信度を考慮可能な深層学習を用いた変状分類の高精度化に関する検討—A Note on Accurate Distress Classification Using Deep Learning Considering Confidence in Attention map—マルチメディアストレージ メディア工学 映像表現&コンピュータグラフィックス
  小川 直輝; 前田 圭介; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 46, 6, 371, 376, Feb. 2022
  東京 : 映像情報メディア学会, Japanese
• 画像中の物体情報を考慮したユーザ類似度に基づく個人に特化した注視領域の推定に関する検討—A Note on Personalized Saliency Prediction Based on User Similarity Considering Object Information in Images—マルチメディアストレージ メディア工学 映像表現&コンピュータグラフィックス
  諸戸 祐哉; 前田 圭介; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 46, 6, 181, 186, Feb. 2022
  東京 : 映像情報メディア学会, Japanese
• 橋梁点検時の技術者の一人称および三人称視点映像を用いた点検動作の分類に関する検討—A Note on Inspection Action Classification Using First and Third Person Video of Engineers Inspecting Bridges—マルチメディアストレージ メディア工学 映像表現&コンピュータグラフィックス
  増田 毅; 前田 圭介; 藤後 廉; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 46, 6, 177, 180, Feb. 2022
  東京 : 映像情報メディア学会, Japanese
• CLASSIFICATION OF INSPECTION ACTION OF ENGINEERS FOR EFFICIENT VIDEO PRESENTATION TO ASSIST ASSESSMENT OF INFRASTRUCTURE FACILITY DISTRESS
  上川恭平; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀, AI・データサイエンス論文集(Web), 3, J2, 811, 818, 2022, [Peer-reviewed]
  In this study, we propose a method for classifying inspection actions of engineers to efficiently search for necessary scenes from inspection videos for the evaluation of distresses in infrastructure facilities. The inspection actions of engineers are more specific than basic actions such as walking and standing, and it is difficult to prepare a large dataset in advance. Therefore, this study proposes a novel classifier for inspection actions using the classification results of basic actions output from a action classification model that has already been trained on a large dataset. Furthermore, by using acton features, object features, and acoustic features, it is feasible to classify inspection actions focusing on the tools used for inspection and the sounds. We demonstrate the effectiveness of the proposed method by quantitatively verifying the accuracy of the inspection action classification and by presenting the classification results to verify the practicality., Japan Society of Civil Engineers, Japanese
• A Note of the Relationship between Popularity Bias and Embedding Representations of Latent Factor Models in Collaborative Filtering
  岡村洋希; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2022, 2022
• A Note on Robust Recommendation System for Domain-dependent Preference Based on Domain-shared Network
  山本一輝; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2022, 2022
• A Note on Image Retrieval Robust for Changing Camera Views Using Synthesized Images by pixelNeRF
  江良勇輝; 藤後廉; 前田圭介; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2022, 2022
• A Note on Uncertainty-based Shoot Event Prediction from Scouting Videos of Soccer by Considering Spatio-Temporal Relations between Players
  五箇亮太; 諸戸祐哉; 前田圭介; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2022, 2022
• A Note on Estimation of Viewed Images Using Brain Activity Data While Viewing Images-A Verification of Estimation Accuracy by Regression Model Used for fMRI Decoder-
  七田亮; 藤後廉; 前田圭介; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2022, 2022
• A Note on Estimation of Diabetic Retinopathy Grades Based on Unsupervised Domain Adaptation Using Fundus Images
  國枝翼; 藤後廉; 前田圭介; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2022, 2022
• A Note on Multi-label Image Recognition in Anime Illustration Based on Graph Convolutional Networks Using Captioning Features
  LAN Ziwen; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告, 46, 6(MMS2022 1-37/ME2022 26-62/AIT2022 1-37), 273, 274, 2022
  IEEE
• A Note on Electron Microscope Image Generation from Mix Proportion and Material Property via Generative Adversarial Network for Rubber Materials
  柳凜太郎; 藤後廉; 前田圭介; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告, 46, 6(MMS2022 1-37/ME2022 26-62/AIT2022 1-37), 2022
• A Note on Improvement of Accuracy in Classification of Destress Images for Efficient Inspection of Road Structures- Introduction of Ratio of Similar Cases Based on Text Data-
  平川泰成; 小川直輝; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告, 46, 6(MMS2022 1-37/ME2022 26-62/AIT2022 1-37), 2022
• A Note on Inspection Action Classification Using First and Third Person Video of Engineers Inspecting Bridges
  増田毅; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告, 46, 6(MMS2022 1-37/ME2022 26-62/AIT2022 1-37), 303, 304, 2022
  IEEE
• A Note on Distress Detection Based on Deep Learning with Hierarchical Multi-Scale Attention Mechanism for Supporting Maintenance of Subway Tunnels
  高田紗弥; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告, 46, 6(MMS2022 1-37/ME2022 26-62/AIT2022 1-37), 2022
• A Note on Perceived Visual Content Estimation Based on Compressed Reconstruction Network Using Brain Signals While Gazing on Images
  東孝明; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告, 46, 6(MMS2022 1-37/ME2022 26-62/AIT2022 1-37), 2022
• A Note on Interest level Estimation Using Users’ behavior Information-Validating the Effectiveness of Feature Integration with Multiple Users-
  上川恭平; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告, 46, 6(MMS2022 1-37/ME2022 26-62/AIT2022 1-37), 2022
• A Note on Visual Sentiment Prediction Based on Few-shot Learning Using Knowledge Distillation
  YE Yingrui; 諸戸祐哉; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告, 46, 6(MMS2022 1-37/ME2022 26-62/AIT2022 1-37), 2022
• Relevance Analysis between Bio-signals of Engineers Inspecting Subway Tunnels and Their Inspection Behaviors
  平澤魁人; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告, 46, 6(MMS2022 1-37/ME2022 26-62/AIT2022 1-37), 2022
• A Note on Personalized Saliency Prediction Based on User Similarity Considering Object Information in Images
  諸戸祐哉; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告, 46, 6(MMS2022 1-37/ME2022 26-62/AIT2022 1-37), 2022
• A Note on Accurate Distress Classification Using Deep Learning Considering Confidence in Attention map
  小川直輝; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告, 46, 6(MMS2022 1-37/ME2022 26-62/AIT2022 1-37), 2022
• 地下鉄トンネルの維持管理支援を目的とした深層学習に基づく変状検出の高精度化に関する検討 : 壁面の施工方法に注目した精度検証—A Note on Improving Performance of Deep Learning-based Distress Detection for Supporting Maintenance of Subway Tunnels : Accuracy Verification Focusing on Tunnel Wall Characteristics—マルチメディアストレージ メディア工学 映像表現&コンピュータグラフィックス
  春山 知生; 前田 圭介; 藤後 廉; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 45, 4, 1, 6, Feb. 2021
  映像情報メディア学会, Japanese
• 地下鉄トンネル点検時の技術者から取得される視線およびモーションデータに基づく熟練度の推定に関する検討—A Note on Estimation of Expert-Novice Level Based on Eye Tracking and Motion Data from Engineers While Inspecting Subway Tunnel—マルチメディアストレージ メディア工学 映像表現&コンピュータグラフィックス
  赤松 祐亮; 前田 圭介; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 45, 4, 7, 12, Feb. 2021
  映像情報メディア学会, Japanese
• 特別講演 路面画像を用いた深層学習に基づく路面状態の分類に関する検討—A Note on Discrimination of Road Surface Conditions Based on Deep Learning Using Road Images—マルチメディアストレージ メディア工学 映像表現&コンピュータグラフィックス
  諸戸 祐哉; 前田 圭介; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 45, 4, 165, 169, Feb. 2021
  映像情報メディア学会, Japanese
• Twitterと映像を用いたMVAEに基づく野球映像の重要シーン予測に関する検討—A Note on Prediction of Important Scenes in Baseball Videos via Multimodal Variational Autoencoder Using Tweets and Videos—マルチメディアストレージ メディア工学 映像表現&コンピュータグラフィックス
  平澤 魁人; 前田 圭介; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 45, 4, 71, 75, Feb. 2021
  映像情報メディア学会, Japanese
• A Verification of Vulnerability of Graph-based Recommender Models under Shilling Attacks
  小野寺望; 前田圭介; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2021, 2021
• A Note on Multi-label Image Recognition in Anime Illustration Based on Graph Convolutional Networks
  LAN Ziwen; 前田圭介; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2021, 2021
• A Note on Introducing Self-supervised Learning to Latent Variable Model for Low-dimensional Feature Extraction
  渡部航史; 前田圭介; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2021, 2021
• An Note on Use of Multiple Datasets in Visual Sentiment Prediction Using Few-shot Learning
  YE Yingrui; 諸戸祐哉; 前田圭介; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2021, 2021
• A Note on Accuracy Assessment Focusing on Manipulation Area by Text-Guided Image Manipulation
  渡邉優宇人; 藤後廉; 前田圭介; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2021, 2021
• A Note on Improvement of Image Sentiment Analysis Based on Introduction of Image Captioning
  LIANG Yun; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告, 45, 4(MMS2021 1-28/ME2021 1-28/AIT2021 1-28), 65, 69, 2021
  東京 : 映像情報メディア学会, Japanese
• A Note on Cross-domain Recommendation Based on Multi-layer Graph Analysis with Visual Features
  平川泰成; 前田圭介; 小川貴弘; 浅水仁; 長谷山美紀, 映像情報メディア学会技術報告, 45, 4(MMS2021 1-28/ME2021 1-28/AIT2021 1-28), 59, 63, 2021
  東京 : 映像情報メディア学会, Japanese
• A Note on Improving Performance of Deep Learning-based Distress Detection for Supporting Maintenance of Subway Tunnels-Accuracy Verification Focusing on Tunnel Wall Characteristics-
  春山知生; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告, 45, 4(MMS2021 1-28/ME2021 1-28/AIT2021 1-28), 2021
• A Note on Estimation of Expert-Novice Level Based on Eye Tracking and Motion Data from Engineers While Inspecting Subway Tunnel
  赤松祐亮; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告, 45, 4(MMS2021 1-28/ME2021 1-28/AIT2021 1-28), 2021
• A Note on Discrimination of Road Surface Conditions Based on Deep Learning Using Road Images
  諸戸祐哉; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告, 45, 4(MMS2021 1-28/ME2021 1-28/AIT2021 1-28), 2021
• A Note on Prediction of Important Scenes in Baseball Videos via Multimodal Variational Autoencoder Using Tweets and Videos
  平澤魁人; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告, 45, 4(MMS2021 1-28/ME2021 1-28/AIT2021 1-28), 2021
• A Note on Accurate Distress Image Classification of Road Structures Using Attention Map Based on Text Data
  小川直輝; 前田圭介; 小川貴弘; 長谷山美紀, 映像情報メディア学会技術報告, 45, 4(MMS2021 1-28/ME2021 1-28/AIT2021 1-28), 17, 21, 2021
  東京 : 映像情報メディア学会, Japanese
• 地下鉄トンネル点検時の生体信号に基づいた熟練および若手技術者の分類に関する検討—A Note on Classification of Experienced and Novice Inspectors Based on Bio-signals While Inspecting in Subway Tunnels—ITS : Intelligent Transport Systems Technology
  九島 哲哉; 前田 圭介; 小川 貴弘; 長谷山 美紀, 電子情報通信学会技術研究報告 = IEICE technical report : 信学技報, 119, 421, 101, 105, Feb. 2020
  電子情報通信学会, Japanese
• 特別講演 社会インフラ維持管理効率化のための最先端AI技術の導入 : 点検データを用いた変状分類の精度向上に向けた取り組み—Introduction of Advanced AI Technology for Efficient Maintenance Inspection of Social Infrastructure : Performance Improvement of Distress Image Classification Utilizing Inspection Data—ITS : Intelligent Transport Systems Technology
  前田 圭介; 小川 貴弘; 長谷山 美紀, 電子情報通信学会技術研究報告 = IEICE technical report : 信学技報, 119, 421, 359, 362, Feb. 2020
  電子情報通信学会, Japanese
• 道路構造物の変状評価における技術者の視線データと熟練度の分析に関する一考察—A Note on Analysis of Gaze Data and Skills of Inspectors in Distress Assessment of Road Structures—マルチメディアストレージ ヒューマンインフォメーション メディア工学 映像表現&コンピュータグラフィックス
  松井 太我; 前田 圭介; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 44, 6, 97, 100, Feb. 2020
  映像情報メディア学会, Japanese
• 道路構造物の維持管理効率化に向けた変状画像分類における信頼性の向上に関する検討—A Note on Improvement of Reliability of Distress Image Classification for Efficient Maintenance of Road Structures—ITS : Intelligent Transport Systems Technology
  堀井 風葉; 前田 圭介; 小川 貴弘; 長谷山 美紀, 電子情報通信学会技術研究報告 = IEICE technical report : 信学技報, 119, 421, 51, 56, Feb. 2020
  電子情報通信学会, Japanese
• 画像注視時のヒトの感情推定のための視線特徴の推定に関する検討—A Note on Estimation of Gaze Features for Human Emotion Estimation while Viewing Images—マルチメディアストレージ ヒューマンインフォメーション メディア工学 映像表現&コンピュータグラフィックス
  諸戸 祐哉; 前田 圭介; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 44, 6, 85, 89, Feb. 2020
  映像情報メディア学会, Japanese
• 道路構造物の変状評価における技術者の視線データと熟練度の分析に関する一考察—A Note on Analysis of Gaze Data and Skills of Inspectors in Distress Assessment of Road Structures—ITS : Intelligent Transport Systems Technology
  松井 太我; 前田 圭介; 小川 貴弘; 長谷山 美紀, 電子情報通信学会技術研究報告 = IEICE technical report : 信学技報, 119, 421, 97, 100, Feb. 2020
  電子情報通信学会, Japanese
• 地下鉄トンネル維持管理支援を目的とした類似画像の検索に関する検討 : 技術者の評価を反映可能な距離計量学習の導入—A Note on Retrieval of Similar Images for Supporting Maintenance of Subway Tunnels : Introduction of Distance Metric Learning Reflecting Inspectors' Evaluation—マルチメディアストレージ ヒューマンインフォメーション メディア工学 映像表現&コンピュータグラフィックス
  松本 有衣; 前田 圭介; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 44, 6, 47, 50, Feb. 2020
  映像情報メディア学会, Japanese
• 特別講演 社会インフラ維持管理効率化のための最先端AI技術の導入 : 点検データを用いた変状分類の精度向上に向けた取り組み—Introduction of Advanced AI Technology for Efficient Maintenance Inspection of Social Infrastructure : Performance Improvement of Distress Image Classification Utilizing Inspection Data—マルチメディアストレージ ヒューマンインフォメーション メディア工学 映像表現&コンピュータグラフィックス
  前田 圭介; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 44, 6, 359, 362, Feb. 2020
  映像情報メディア学会, Japanese
• 地下鉄トンネル維持管理支援を目的とした類似画像の検索に関する検討 : 技術者の評価を反映可能な距離計量学習の導入—A Note on Retrieval of Similar Images for Supporting Maintenance of Subway Tunnels : Introduction of Distance Metric Learning Reflecting Inspectors' Evaluation—ITS : Intelligent Transport Systems Technology
  松本 有衣; 前田 圭介; 小川 貴弘; 長谷山 美紀, 電子情報通信学会技術研究報告 = IEICE technical report : 信学技報, 119, 421, 47, 50, Feb. 2020
  電子情報通信学会, Japanese
• 地下鉄トンネル点検時の生体信号に基づいた熟練および若手技術者の分類に関する検討—マルチメディアストレージ ヒューマンインフォメーション メディア工学 映像表現&コンピュータグラフィックス
  九島 哲哉; 前田 圭介; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 44, 6, 101, 105, Feb. 2020
  映像情報メディア学会, Japanese
• 画像注視時のヒトの感情推定のための視線特徴の推定に関する検討—A Note on Estimation of Gaze Features for Human Emotion Estimation while Viewing Images—ITS : Intelligent Transport Systems Technology
  諸戸 祐哉; 前田 圭介; 小川 貴弘; 長谷山 美紀, 電子情報通信学会技術研究報告 = IEICE technical report : 信学技報, 119, 421, 85, 89, Feb. 2020
  電子情報通信学会, Japanese
• 道路構造物の維持管理効率化に向けた変状画像分類における信頼性の向上に関する検討—A Note on Improvement of Reliability of Distress Image Classification for Efficient Maintenance of Road Structures—マルチメディアストレージ ヒューマンインフォメーション メディア工学 映像表現&コンピュータグラフィックス
  堀井 風葉; 前田 圭介; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 44, 6, 51, 56, Feb. 2020
  映像情報メディア学会, Japanese
• Introduction of Advanced AI Technology for Efficient Maintenance Inspection of Social Infrastructure-Performance Improvement of Distress Image Classification Utilizing Inspection Data-
  前田圭介; 小川貴弘; 長谷山美紀, 電子情報通信学会技術研究報告, 119, 421(ITS2019 30-56), 2020
• A Note on Improvement of Reliability of Dstress Image Classification for Efficient Maintenace of Road Structures
  堀井風葉; 前田圭介; 小川貴弘; 長谷山美紀, 電子情報通信学会技術研究報告, 119, 421(ITS2019 30-56), 2020
• A Note on Retrieval of Similar Images for Supporting Maintenance of Subway Tunnels-Introduction of Distance Metric Learning Reflecting Inspectors’ Evaluation-
  松本有衣; 前田圭介; 小川貴弘; 長谷山美紀, 電子情報通信学会技術研究報告, 119, 421(ITS2019 30-56), 2020
• A Note on Classification of Experienced and Novice Inspectors Based on Bio-signals While Inspecting in Subway Tunnels
  九島哲哉; 前田圭介; 小川貴弘; 長谷山美紀, 電子情報通信学会技術研究報告, 119, 421(ITS2019 30-56), 2020
• A Note on Analysis of Gaze Data and Skills of Inspectors in Distress Assessment of Road Structures
  松井太我; 前田圭介; 小川貴弘; 長谷山美紀, 電子情報通信学会技術研究報告, 119, 421(ITS2019 30-56), 2020
• A Note on Estimation of Gaze Features for Human Emotion Estimation while Viewing Images
  諸戸祐哉; 前田圭介; 小川貴弘; 長谷山美紀, 電子情報通信学会技術研究報告, 119, 421(ITS2019 30-56), 2020
• A Note on Discrimination of Road Surface Conditions Based on Anomaly Detection Using Road Images
  諸戸祐哉; 前田圭介; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2020, 2020
• A Note on Cross-domain Recommendation Based on Multilayer Graph Analysis-Performance Evaluation with Change in Dimension of Embedding Feature-
  平川泰成; 前田圭介; 小川貴弘; 浅水仁; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2020, 2020
• A Note on User Interest Estimation Based on Feature Integration Using m-SimGP
  上川恭平; 前田圭介; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2020, 2020
• A Note on Image Sentiment Analysis based on Multi-level Deep Metric Net
  LIANG Yun; 前田圭介; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2020, 2020
• A Note on Improvement of Distress Detection Performance in Subway Tunnel Images by Data Augmentation Based on RICAP
  春山知生; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2020, 2020
• A Note of Estimation of Viewed Images Using fMRI Data While Viewing Images-Introduction of Shared Brain Responses of Multiple Subjects Based on Probabilistic Generative Model-
  東孝明; 前田圭介; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2020, 2020
• SIMILAR INSPECTION DATA RETRIEVAL FOR ROAD STRUCTURE INSPECTION BASED ON CANONICAL CORRELATION BETWEEN EYE TRACKING DATA AND INSPECTION RECORDS
  前田圭介; 斉藤僚汰; 高橋翔; 小川貴弘; 長谷山美紀, 土木学会論文集 F3(土木情報学)(Web), 76, 1, 2020
• A Note on User-specific Visual Attention Estimation Based on Visual and Spatial Information in Images : A Study on Relationship between Estimation Performance and the Similarity of Visual Features
  諸戸 祐哉; 前田 圭介; 小川 貴弘; 長谷山 美紀, 電子情報通信学会技術研究報告 = IEICE technical report : 信学技報, 119, 132, 13, 16, 19 Jul. 2019
  電子情報通信学会, Japanese
• A Note on User-specific Visual Attention Estimation Based on Visual and Spatial Information in Images : A Study on Relationship between Estimation Performance and the Similarity of Visual Features
  諸戸 祐哉; 前田 圭介; 小川 貴弘; 長谷山 美紀, 電子情報通信学会技術研究報告 = IEICE technical report : 信学技報, 119, 132, 13, 16, 19 Jul. 2019
  電子情報通信学会, Japanese
• A Note on Accurate Estimation of Deterioration Levels on Transmission Towers via Deep Learning Using Heterogeneous Features
  前田 圭介; 高橋 翔; 小川 貴弘; 長谷山 美紀, 電子情報通信学会技術研究報告 = IEICE technical report : 信学技報, 118, 449, 361, 364, 19 Feb. 2019
  電子情報通信学会, Japanese
• A note on estimation of inspectors' visual attention using distress images of subway tunnels : Trial introduction of deep learning-based saliency prediction methods
  斉藤 僚汰; 前田 圭介; 小川 貴弘; 長谷山 美紀, 電子情報通信学会技術研究報告 = IEICE technical report : 信学技報, 118, 449, 281, 285, 19 Feb. 2019
  電子情報通信学会, Japanese
• A Note on Attribute Estimation for Improving Interpretability of Convolutional Neural Network
  堀井 風葉; 前田 圭介; 小川 貴弘; 長谷山 美紀, 電子情報通信学会技術研究報告 = IEICE technical report : 信学技報, 118, 449, 275, 279, 19 Feb. 2019
  電子情報通信学会, Japanese
• A Note on Accurate Estimation of Deterioration Levels on Transmission Towers via Deep Learning Using Heterogeneous Features
  前田 圭介; 高橋 翔; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 43, 5, 361, 364, Feb. 2019
  映像情報メディア学会, Japanese
• A note on estimation of inspectors' visual attention using distress images of subway tunnels : Trial introduction of deep learning-based saliency prediction methods
  斉藤 僚汰; 前田 圭介; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 43, 5, 281, 285, Feb. 2019
  映像情報メディア学会, Japanese
• A Note on Attribute Estimation for Improving Interpretability of Convolutional Neural Network
  堀井 風葉; 前田 圭介; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 43, 5, 275, 279, Feb. 2019
  映像情報メディア学会, Japanese
• 変状分類におけるGrad-CAM++に基づいたCNNの注目領域の可視化に関する検討
  小川直輝; 前田圭介; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2019, 2019
• Sparse Bayesian Learningに基づく注視領域の時間変化を考慮したヒトの感情推定に関する検討
  諸戸祐哉; 前田圭介; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2019, 2019
• Twitterを用いた異常検知に基づく野球映像の重要シーン検出に関する検討
  平澤魁人; 前田圭介; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2019, 2019
• 画像注視時の注視領域の時間変化を考慮したテンソル解析に基づく感情推定に関する検討
  諸戸祐哉; 前田圭介; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2018, ROMBUNNO.92, 27 Oct. 2018
  Japanese
• 地下鉄トンネルの点検における視線データを用いた熟練度の分析に関する一考察
  斉藤僚汰; 前田圭介; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2018, ROMBUNNO.11, 27 Oct. 2018
  Japanese
• Trial Introduction of Convolutional Sparse Coding for Accurate Distress Classification of Road Structures
  前田 圭介; 高橋 翔; 小川 貴弘; 長谷山 美紀, 電子情報通信学会技術研究報告 = IEICE technical report : 信学技報, 117, 432, 189, 194, 15 Feb. 2018
  電子情報通信学会, Japanese
• Trial Introduction of Convolutional Sparse Coding for Accurate Distress Classification of Road Structures
  前田 圭介; 高橋 翔; 小川 貴弘; 長谷山 美紀, 電子情報通信学会技術研究報告 = IEICE technical report : 信学技報, 117, 432, 189, 194, 15 Feb. 2018
  電子情報通信学会, Japanese
• Trial Introduction of Convolutional Sparse Coding for Accurate Distress Classification of Road Structures
  前田 圭介; 高橋 翔; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 42, 4, 189, 194, Feb. 2018
  映像情報メディア学会, Japanese
• 画像特徴量とfNIRS特徴量の関連性に注目した画像分類の高精度化に関する検討
  堀井風葉; 前田圭介; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2017, ROMBUNNO.114, 28 Oct. 2017
  Japanese
• 正準相関最大化を導入した深層学習に基づく送電鉄塔の劣化レベル分類に関する検討 (メディア工学) -- (サマーセミナー2017 : 世界に羽ばたくビジョン技術)
  前田 圭介; 高橋 翔; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 41, 29, 11, 14, Sep. 2017
  本文では, 正準相関最大化を導入した深層学習に基づく送電鉄塔の劣化レベル分類手法を提案する. 送電鉄塔の点検において, 技術者は, 劣化部位の画像に加え, 点検中の鉄塔に関する種々のテキストデータを記録する. そのため, 深層学習の中間層から得られる画像特徴のみならず, テキストデータから得られる特徴 (以降, テキスト特徴)を利用することで, 劣化レベルの分類の精度向上が期待される. そこで, 本文では, 深層学習の1つであるDeep Extreme Learning Machine-Local Receptive Fieldの中間層から得られる画像特徴をテキスト特徴を考慮した特徴へ変換するための中間層を構築する. 具体的に, 異種特徴間の解析に用いられる正準相関分析を用いることで画像特徴とテキスト特徴を互いに比較可能な特徴空間へ射影し, それらの相関を最大化するような特徴へ変換することで, 送電鉄塔の劣化レベル分類を行う. 提案手法を用いることで, 中間層における相関最大化を行わない深層学習手法よりも高精度な劣化レベル分類が実現する., 映像情報メディア学会, Japanese
• A Note on Accurate Distress Classification for Maintenance Inspection of Road Structures via Deep Learning
  前田 圭介; 高橋 翔; 小川 貴弘; 長谷山 美紀, 電子情報通信学会技術研究報告 = IEICE technical report : 信学技報, 116, 464, 51, 54, 20 Feb. 2017
  電子情報通信学会, Japanese
• A Note on Accurate Distress Classification for Maintenance Inspection of Road Structures via Deep Learning
  前田 圭介; 高橋 翔; 小川 貴弘; 長谷山 美紀, 電子情報通信学会技術研究報告 = IEICE technical report : 信学技報, 116, 464, 51, 54, 20 Feb. 2017
  電子情報通信学会, Japanese
• A Note on Accurate Distress Classification for Maintenance Inspection of Road Structures via Deep Learning
  前田 圭介; 高橋 翔; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 41, 5, 51, 54, Feb. 2017
  Automatic distress classification of distresses occurring in road structures is necessary in order to support inspectors for maintenance inspection. This paper presents distress classification method using deep learning for improving classification performance. Specifically, the proposed method generates a classifier based on Deep Extreme Learning Machine which is one of deep learning methods, constructs Auto Encoder for each hidden layer and sequentially determines parameters between hidden layers. Consequently, realization of more accurate distress classification is expected compared to previously machine learning methods., 映像情報メディア学会, Japanese
• A Note on Accurate Distress Classification for Maintenance Inspection of Road Structures : Integration of Multiple Classification Results based on Tag Data and Distress Images
  前田 圭介; 高橋 翔; 小川 貴弘, 電子情報通信学会技術研究報告 = IEICE technical report : 信学技報, 115, 459, 181, 184, 22 Feb. 2016
  電子情報通信学会, Japanese
• A Note on Accurate Distress Classification for Maintenance Inspection of Road Structures : Integration of Multiple Classification Results based on Tag Data and Distress Images
  前田 圭介; 高橋 翔; 小川 貴弘, 電子情報通信学会技術研究報告 = IEICE technical report : 信学技報, 115, 458, 181, 184, 22 Feb. 2016
  電子情報通信学会, Japanese
• A Note on Accurate Distress Classification for Maintenance Inspection of Road Structures : Integration of Multiple Classification Results based on Tag Data and Distress Images
  前田 圭介; 高橋 翔; 小川 貴弘; 長谷山 美紀, 映像情報メディア学会技術報告 = ITE technical report, 40, 6, 181, 184, Feb. 2016
  映像情報メディア学会, Japanese
• 20190725
  前田 圭介; 小川 貴弘; 長谷山 美紀, ITE Technical Report, 40, 0, 47, 48, 2016
  近年,人類の生存可能な惑星として ,火星に関する研究が盛んに行われている.特に,火星の気象環境に大きな影響を及ぼす dust stormと呼ばれる砂塵の嵐の検出に関する研究は種々行われている.しかしながら, dust stormの検出は,従来より手動で行われていることから,我々は dust stormの自動検出手法の構築を目指す.一般に, dust stormの存在する画像数は,存在しない画像数と比較して少ないため,学習データに偏りが存在する.そこで,本文では,データ数の少ない dust stormが存在するクラスの特徴ベクトルを人工的に生成することで,これらの不均衡データを考慮した dust storm識別手法を提案する., The Institute of Image Information and Television Engineers, Japanese
• 個々の道路構造物に関する点検項目の導入による道路構造物の変状推定の高精度化に関する検討
  前田圭介; 高橋翔; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2015, ROMBUNNO.133, 07 Nov. 2015
  Japanese
• 複数の画像特徴を用いたベイジアンネットワークに基づく構造物の変状の推定の高精度化に関する検討
  前田圭介; 高橋翔; 小川貴弘; 長谷山美紀, 電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2014, ROMBUNNO.140, 25 Oct. 2014
  Japanese

• AI research for DX realization in the field of infrastructure maintenance
  前田圭介; 小川貴弘; 長谷山美紀
  2024

• Multi-label Image Recognition Based on Multi-modal Graph Convolutional Networks Using Captioning Features.
  Ziwen Lan; Keisuke Maeda; Takahiro Ogawa 0001; Miki Haseyama
  GCCE, 2021
  2021 - 2021
• A Note on Accurate Distress Image Classification of Road Structures Using Attention Map Based on Text Data
  小川直輝; 前田圭介; 小川貴弘; 長谷山美紀
  映像情報メディア学会技術報告, 2021
  2021 - 2021
• A Note on Prediction of Important Scenes in Baseball Videos via Multimodal Variational Autoencoder Using Tweets and Videos
  平澤魁人; 前田圭介; 小川貴弘; 長谷山美紀
  映像情報メディア学会技術報告, 2021
  2021 - 2021
• A Note on Discrimination of Road Surface Conditions Based on Deep Learning Using Road Images
  諸戸祐哉; 前田圭介; 小川貴弘; 長谷山美紀
  映像情報メディア学会技術報告, 2021
  2021 - 2021
• A Note on Estimation of Expert-Novice Level Based on Eye Tracking and Motion Data from Engineers While Inspecting Subway Tunnel
  赤松祐亮; 前田圭介; 小川貴弘; 長谷山美紀
  映像情報メディア学会技術報告, 2021
  2021 - 2021
• A Note on Improving Performance of Deep Learning-based Distress Detection for Supporting Maintenance of Subway Tunnels-Accuracy Verification Focusing on Tunnel Wall Characteristics-
  春山知生; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀
  映像情報メディア学会技術報告, 2021
  2021 - 2021
• A Note on Cross-domain Recommendation Based on Multi-layer Graph Analysis with Visual Features
  平川泰成; 前田圭介; 小川貴弘; 浅水仁; 長谷山美紀
  映像情報メディア学会技術報告, 2021
  2021 - 2021
• A Note on Improvement of Image Sentiment Analysis Based on Introduction of Image Captioning
  LIANG Yun; 前田圭介; 小川貴弘; 長谷山美紀
  映像情報メディア学会技術報告, 2021
  2021 - 2021
• SIMILAR INSPECTION DATA RETRIEVAL FOR ROAD STRUCTURE INSPECTION BASED ON CANONICAL CORRELATION BETWEEN EYE TRACKING DATA AND INSPECTION RECORDS
  前田圭介; 斉藤僚汰; 高橋翔; 小川貴弘; 長谷山美紀
  土木学会論文集 F3(土木情報学)(Web), 2020
  2020 - 2020
• A Note of Estimation of Viewed Images Using fMRI Data While Viewing Images-Introduction of Shared Brain Responses of Multiple Subjects Based on Probabilistic Generative Model-
  東孝明; 前田圭介; 小川貴弘; 長谷山美紀
  電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2020
  2020 - 2020
• A Note on Improvement of Distress Detection Performance in Subway Tunnel Images by Data Augmentation Based on RICAP
  春山知生; 前田圭介; 藤後廉; 小川貴弘; 長谷山美紀
  電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2020
  2020 - 2020
• A Note on Image Sentiment Analysis based on Multi-level Deep Metric Net
  LIANG Yun; 前田圭介; 小川貴弘; 長谷山美紀
  電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2020
  2020 - 2020
• A Note on User Interest Estimation Based on Feature Integration Using m-SimGP
  上川恭平; 前田圭介; 小川貴弘; 長谷山美紀
  電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2020
  2020 - 2020
• A Note on Cross-domain Recommendation Based on Multilayer Graph Analysis-Performance Evaluation with Change in Dimension of Embedding Feature-
  平川泰成; 前田圭介; 小川貴弘; 浅水仁; 長谷山美紀
  電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2020
  2020 - 2020
• A Note on Discrimination of Road Surface Conditions Based on Anomaly Detection Using Road Images
  諸戸祐哉; 前田圭介; 小川貴弘; 長谷山美紀
  電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM), 2020
  2020 - 2020
• A Note on Estimation of Gaze Features for Human Emotion Estimation while Viewing Images
  諸戸祐哉; 前田圭介; 小川貴弘; 長谷山美紀
  電子情報通信学会技術研究報告, 2020
  2020 - 2020
• A Note on Analysis of Gaze Data and Skills of Inspectors in Distress Assessment of Road Structures
  松井太我; 前田圭介; 小川貴弘; 長谷山美紀
  電子情報通信学会技術研究報告, 2020
  2020 - 2020
• A Note on Classification of Experienced and Novice Inspectors Based on Bio-signals While Inspecting in Subway Tunnels
  九島哲哉; 前田圭介; 小川貴弘; 長谷山美紀
  電子情報通信学会技術研究報告, 2020
  2020 - 2020
• A Note on Retrieval of Similar Images for Supporting Maintenance of Subway Tunnels-Introduction of Distance Metric Learning Reflecting Inspectors’ Evaluation-
  松本有衣; 前田圭介; 小川貴弘; 長谷山美紀
  電子情報通信学会技術研究報告, 2020
  2020 - 2020
• A Note on Improvement of Reliability of Dstress Image Classification for Efficient Maintenace of Road Structures
  堀井風葉; 前田圭介; 小川貴弘; 長谷山美紀
  電子情報通信学会技術研究報告, 2020
  2020 - 2020
• Introduction of Advanced AI Technology for Efficient Maintenance Inspection of Social Infrastructure-Performance Improvement of Distress Image Classification Utilizing Inspection Data-
  前田圭介; 小川貴弘; 長谷山美紀
  電子情報通信学会技術研究報告, 2020
  2020 - 2020
• Trial Introduction of Convolutional Sparse Coding for Accurate Distress Classification of Road Structures
  前田 圭介; 高橋 翔; 小川 貴弘; 長谷山 美紀
  映像情報メディア学会技術報告 = ITE technical report, Feb. 2018, Japanese
• 正準相関最大化を導入した深層学習に基づく送電鉄塔の劣化レベル分類に関する検討 (メディア工学) -- (サマーセミナー2017 : 世界に羽ばたくビジョン技術)
  前田 圭介; 高橋 翔; 小川 貴弘; 長谷山 美紀
  映像情報メディア学会技術報告 = ITE technical report, Sep. 2017, Japanese
• A Note on Accurate Distress Classification for Maintenance Inspection of Road Structures via Deep Learning
  前田 圭介; 高橋 翔; 小川 貴弘; 長谷山 美紀
  映像情報メディア学会技術報告 = ITE technical report, Feb. 2017, Japanese
• A Note on Accurate Distress Classification for Maintenance Inspection of Road Structures : Integration of Multiple Classification Results based on Tag Data and Distress Images
  前田 圭介; 高橋 翔; 小川 貴弘; 長谷山 美紀
  映像情報メディア学会技術報告 = ITE technical report, Feb. 2016, Japanese

• THE INSTITUTE OF ELECTRONICS
• IEEE

• 専門知識の抽出・更新・共有を可能とするインフラ特化型LMMの構築
  BOOST若手
  Nov. 2025 - Oct. 2030
  前田圭介
  JST, Principal investigator, JPMJBY24G9
• 専門知識の抽出・更新・共有を可能とするインフラ特化型LMMの構築
  2025 - 2030
  前田 圭介
  本研究では、生体情報解析と大規模視覚言語モデル（Large Multi-modal Model: LMM）を融合し、インフラ維持管理分野における専門知識の抽出・更新・共有を可能とする持続可能な次世代生成AI理論「インフラ特化型LMM」を構築します。生体情報を用いた新たな知識獲得アプローチを提示する本研究により、蓄積されたデータに依存した現在の学習理論の限界を打破し、インフラ維持管理分野におけるLMMの精度限界にブレークスルーを与えます。
  科学技術振興機構, 戦略的な研究開発の推進/国家戦略分野の若手研究者及び博士後期課程学生の育成事業（BOOST）/次世代AI人材育成プログラム(若手研究者支援), 24036835
• 画像認識の高度化に向けた画像の撮影方法を最適化する異環境異種データ適応型AIの構築
  科学研究費助成事業
  01 Apr. 2023 - 31 Mar. 2027
  前田 圭介
  本研究課題では、インフラ維持管理の現場で撮影される損傷画像の認識精度向上のために、AIに入力される画像の撮影方法を最適化する異環境異種データ適応型AIの構築を目指す。これまで申請者が進めてきた「損傷画像中の注目領域を推定する説明可能なAI（XAI）」をインフラ点検中の技術者視点の映像へ対応可能となるよう拡張することで、AIの実社会応用で生じる画像撮影方法の多様性の問題を解決可能な新たな理論を構築する。本理論構築に向けて、【フェーズ１】１人称映像と損傷画像の関係性を学習する異種データ対照学習理論の実現、【フェーズ２】附帯情報を導入した異環境適応型XAIの実現、【フェーズ３】技術者間のノウハウの共通性を転移可能なマルチビューグラフ埋め込み理論の構築、【フェーズ４】プロトタイプ版の構築と技術者からのフィードバックの反映の４つのフェーズに分けて実施する。
  令和５年度では、【フェーズ１】の実現に向けて、損傷画像を用いて構築したAIにインフラ点検時に得られた映像を入力することで、異なる種類のデータであっても損傷を検出可能であるかを検証した。検証結果より、点検時に得られた映像から損傷を高精度に検出可能であることが明らかとなったことから、異種データ間の関係性を学習可能な理論の構築を実現した。さらに、本計画当初は技術者による現場点検を想定していたが、更なる省力化のためにはドローンの活用が期待されていることから、難易度の高いドローン映像へ適用し、研究を進めてきた。上記に関連する研究の成果が認められ、査読付き学術論文誌への採録、さらに映像情報メディア学会の特別講演に至っている。
  日本学術振興会, 基盤研究(C), 北海道大学, 23K11211
• General-purpose deep learning theory for ultra-low computational complexity and low capacity in the age of edge AI
  Grants-in-Aid for Scientific Research
  01 Apr. 2021 - 31 Mar. 2026
  小川 貴弘; 藤後 廉; 前田 圭介
  本研究課題では、エッジAI時代の超低演算量・低容量化を実現する汎用深層学習理論の構築を目指している。研究代表者が進めてきた低演算量・低容量バイナリスパース表現技術とクロスモーダル埋め込み技術の研究を融合させ、AIの演算量と学習データ量を大幅に削減可能な新たな理論を構築する。具体的に、最先端の深層学習モデルをバイナリスパース表現により模倣し、さらに、他のモダリティからの知識転移を行うことで、深層学習の利点である高い精度を保持しつつ、演算量削減と学習データ量の小規模化を同時に実現する。本研究課題では、構築理論の汎用性を示すとともに、エッジデバイス上での評価検証を行う。尚、本研究課題は研究分担者とともに遂行し、実施項目である「① モデルクローニング技術の実現による演算量の削減」および「② クロスモーダル知識転移技術の実現による学習データ量の小規模化」については、①の研究を小川・藤後が、②の研究を小川・前田が実施する。
  令和4年度は、「バイナリスパース深層学習モデルの実現」を目指し、研究を遂行した。具体的に、演算量削減と学習データ量の小規模化のそれぞれを以下のように実現した。まず、構築済みの「深層学習モデルの中間層出力」と「バイナリスパース深層学習モデルの中間出力」との相関を最大化する理論に、データの近似誤差最小化を可能にする損失関数を新たに組み込むことで、各中間層出力を低演算量のバイナリスパース表現で模倣するモデルクローニングを実現した。次に、異なる種類のモダリティの相関を最大化する理論を構築することで、学習データ量の不足をモダリティ相関に基づき補間するクロスモーダル知識転移を実現した。研究成果の対外発表についても積極的に行い、コンピュータビジョン分野のトップ国際会議ECCVへの採択や、信号処理分野のトップ国際会議ICASSPへの採択に至った。
  Japan Society for the Promotion of Science, Grant-in-Aid for Scientific Research (B), Hokkaido University, 23K21676
• General-purpose deep learning theory for ultra-low computational complexity and low capacity in the age of edge AI
  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B)
  01 Apr. 2021 - 31 Mar. 2026
  小川 貴弘; 前田 圭介; 藤後 廉
  本研究課題では、エッジAI時代の超低演算量・低容量化を実現する汎用深層学習理論の構築を目指す。研究代表者が進めてきた低演算量・低容量バイナリスパース表現技術とクロスモーダル埋め込み技術の研究を融合させ、AIの演算量と学習データ量を大幅に削減可能な新たな理論を構築する。具体的に、最先端の深層学習モデルをバイナリスパース表現により模倣し、さらに、他のモダリティからの知識転移を行うことで、深層学習の利点である高い精度を保持しつつ、演算量削減と学習データ量の小規模化を同時に実現する。本研究課題では、構築した理論が汎用性を有することを示すとともに、エッジデバイス上での評価検証を行う。尚、本研究課題は研究分担者とともに遂行し、実施項目である「① モデルクローニング技術の実現による演算量の削減」および「② クロスモーダル知識転移技術の実現による学習データ量の小規模化」については、①の研究を小川・藤後が、②の研究を小川・前田が実施する。
  令和3年度は、「深層学習モデルにおける中間層出力」と「バイナリスパース表現係数」との間で相関を最大化するクロスモーダル埋め込み理論を構築した。具体的に、ソースドメインに対応する実数データとバイナリスパース表現係数との間でクロスモーダル埋め込みを行い、それらの相関が最大化されるよう、バイナリスパース表現における辞書学習を可能とした。この際、バイナリスパース表現係数は0または1の疎なデータであることに注目し、観測データがバイナリスパース値である制約を設けた新たなクロスモーダル埋め込み理論を実現した。さらに、構築した理論やその応用に関する研究成果の対外発表についても積極的に行い、クロスモーダル埋め込み理論を応用した研究成果が画像処理分野における世界最高峰の国際会議ICIP等に採択されている。
  Japan Society for the Promotion of Science, Grant-in-Aid for Scientific Research (B), Hokkaido University, 21H03456
• 技術者の認知・判断・行動プロセスに基づくハイパーマルチモーダル画像分類技術の構築
  科学研究費助成事業 若手研究
  01 Apr. 2020 - 31 Mar. 2024
  前田 圭介
  AI技術の応用が期待される専門分野において，高い精度のみならず結果に対する確かな信頼性を有するモデルを構築することが本研究の目的である．このモデルの構築のためには，次の４点を組み込んだ機械学習理論の構築が必要である．【要点１】経験や知識の基となる情報を機械に入力可能な特徴へ変換するモデル．【要点２】経験や知識を表す生体特徴を抽出することで技術者に近い判断が可能なモデル．【要点３】画像分類で判断する際の判断根拠を技術者が理解可能な形で提示するモデル．【要点４】得られた結果が誤っていた場合に，効率よく再学習可能なモデル．そこで，本研究では，【解決策１】複数の技術者から得られる種々の生体情報からノイズ・個人差を除去．【解決策２】複数の技術者に共通する特徴を見出し，技術者の判断との間の因果関係を説明．【解決策３】判断・予想結果に対する判断根拠の可視化機構を導入．【解決策４】技術者からのフィードバック結果を用いたモデルパラメータの転移学習の４つの解決策により，上記要点を満たした新たな機械学習理論を導出する．
  
  
  令和二年度では，【解決策１】に対応する【フェーズ１】複数の技術者から多種多様な生体情報の取得およびその特徴量化を実施した．具体的に，技術者から，視線・脳活動・動作などの生体情報と遂行業務に対する判断を取得した．さらに，技術者に共通する特徴を明らかにするために，複数の技術者から得られる生体データと遂行業務に対する判断との間の関連性について分析を実施した．これにより，一部の生体データがAI構築に特に有効であることが示唆され，これらのデータの特徴量化を行った．上述の研究によって得られた結果および関連する成果を信号処理・画像処理系の国内会議・国際会議において発表を行い，加えて，学会発表で得られた知見に基づき，手法を高度化することにより，学術論文誌にも採録された．
  日本学術振興会, 若手研究, 北海道大学, 20K19856
• 専門家の認知プロセスを模擬した深層学習に基づく画像分類技術の構築
  Grants-in-Aid for Scientific Research Grant-in-Aid for JSPS Fellows
  Apr. 2018 - Feb. 2020
  前田 圭介
  本研究は，土木工学・医学・惑星科学などの専門分野へのAI技術の実用化に向けて，専門家から経験や知識を抽出することで, 専門家に近い判断が可能な画像分類技術を構築することを目的としている．平成３０年度は，以下に示す２点の研究を行った．
  １．専門家の注視領域に基づく画像特徴 (視線特徴) の算出
  画像中の物体に関する特徴（画像特徴）と, 人間が画像を注視している際の視線情報を用いて, 注視領域に基づいた視線特徴の算出を可能とする理論を構築した. 具体的に, 実験協力者に画像を注視してもらい, 視線の停留と動き方から注視領域を推定し, 当該領域から得られる特徴を視線特徴として算出した．また, 画像特徴と視線特徴間の関係を学習させることで, 注目領域が未知であるテストデータが入力された際に, 人間が注目すると考えられる領域を自動推定する技術も同時に構築した．さらに, 専門家の注視領域のみならず, 画像注視中の人間の脳から得られる情報を用いた画像分類手法を構築した. 本手法では, functional Near-Infrared Spectroscopy (fNIRS) を用いて脳の情報を取得し, 土木分野で用いられる画像を用いた分類の高精度化を実現した．
  ２．画像特徴から視線特徴を推定するための射影行列の算出
  画像特徴から視線特徴を推定するための射影行列を算出可能とする理論を構築した. 具体的に, 異なる種類の特徴の相関関係を分析することで, それらの特徴間を関連付ける射影行列の導出を可能とした．さらに, 上記の手法で構築した理論を画像特徴と視線特徴に応用することで, 画像特徴から視線特徴を関連付ける射影行列の導出を可能とした．
  Japan Society for the Promotion of Science, Grant-in-Aid for JSPS Fellows, Hokkaido University, Principal investigator, 18J10373

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